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[Перевод] Демистифицируем парсинг PDF: конвейерная обработка

Обзор, способы реализации и выводы

Преобразование неструктурированных документов, таких как PDF-файлы и отсканированные изображения, в структурированные или полуструктурированные форматы является важной составляющей искусственного интеллекта. Однако из-за замысловатой природы PDF-файлов и сложности задач, связанных с парсингом PDF, этот процесс не кажется на первый взгляд таким уж очевидным.

Этот цикл статей посвящен демистификации парсинга PDF. В предыдущей статье мы описали основную задачу парсинга PDF, классифицировали существующие методы и дали краткое описание каждого из них.

В этой статье мы сосредоточимся на конвейерном подходе. Мы начнем с обзора самого метода, затем продемонстрируем несколько стратегий по его реализации на примере готовых фреймворков, специализирующихся на этой задаче и, наконец, проанализируем полученные результаты.

Обзор

Конвейерный подход рассматривает задачу парсинга PDF-файлов как показано на рисунке 1.

Рисунок 1: Общий алгоритм конвейерного подхода. Изображение автора.

Конвейерный подход можно разделить на следующие пять этапов:

Предобработка PDF-файлов целью исправления таких проблем, как размытость или перекос в ориентации страниц. Этот этап включает в себя повышение качества изображения, коррекцию положения и т. д.
Проведение анализа макета, который можно разделить на два этапа: визуальный и семантический анализ структуры. Первый выявляет структуру документа и выделяет схожие области, а второй маркирует эти области определенными типами, такими как текст, заголовок, список, таблица, рисунок и т. д. На этом этапе также определяется порядок чтения страницы.
Отделение различных областей, выявленных в ходе анализа макета, друг от друга. Этот процесс включает в себя распознавание таблиц, текста и определение других компонентов, таких как формулы, блок-схемы и специальные символы.
Воспроизведение структуры страницы документа на основе полученных ранее результатов.
Вывод структурированной или полуструктурированной информации, например, в формате Markdown, JSON или HTML.

Теперь мы с вами рассмотрим несколько фреймворков для парсинга PDF. Таким образом мы сможем сформировать представление о конвейерном подходе и получить некоторые выводы, о которых мы поговорим в конце.

Marker

Marker — это конвейер на основе моделей глубокого обучения. Он способен конвертировать PDF-, EPUB- и MOBI-документы в формат Markdown.

Общий процесс

Как показано на рисунке 2, процесс работы с Marker разделен на следующие четыре этапа:

Рисунок 2: Конвейер Marker. Изображение автора.

Шаг 1: Для начала разделим страницы на блоки и извлечем текст с помощью PyMuPDF и OCR. Ниже приведен соответствующий код:

def convert_single_pdf(
        fname: str,
        model_lst: List,
        max_pages=None,
        metadata: Optional[Dict]=None,
        parallel_factor: int = 1
) -> Tuple[str, Dict]:
    ...
    ...
    doc = pymupdf.open(fname, filetype=filetype)
    if filetype != "pdf":
        conv = doc.convert_to_pdf()
        doc = pymupdf.open("pdf", conv)


    blocks, toc, ocr_stats = get_text_blocks(
        doc,
        tess_lang,
        spell_lang,
        max_pages=max_pages,
        parallel=int(parallel_factor * settings.OCR_PARALLEL_WORKERS)
    )

Шаг 2: Используем сегментатор макета, чтобы выделить отдельные блоки, и упорядочим их с помощью детектора колонок. Соответствующий код имеет вид:

def convert_single_pdf(
        fname: str,
        model_lst: List,
        max_pages=None,
        metadata: Optional[Dict]=None,
        parallel_factor: int = 1
) -> Tuple[str, Dict]:
    ...
    ...
    # Распаковка моделей из списка


    texify_model, layoutlm_model, order_model, edit_model = model_lst


    block_types = detect_document_block_types(
        doc,
        blocks,
        layoutlm_model,
        batch_size=int(settings.LAYOUT_BATCH_SIZE * parallel_factor)
    )


    # Поиск верхних и нижних колонтитулов


    bad_span_ids = filter_header_footer(blocks)
    out_meta["block_stats"] = {"header_footer": len(bad_span_ids)}


    annotate_spans(blocks, block_types)


    # Выгрузка отладочных данных, если установлены соответствующие флаги


    dump_bbox_debug_data(doc, blocks)


    blocks = order_blocks(
        doc,
        blocks,
        order_model,
        batch_size=int(settings.ORDERER_BATCH_SIZE * parallel_factor)
    )
    ...
    ...

Шаг 3: Отфильтруем верхние и нижние колонтитулы, исправим блоки с кодом и таблицами и применим модель Texify для формул. Соответствующий код выглядит следующим образом:

def convert_single_pdf(
        fname: str,
        model_lst: List,
        max_pages=None,
        metadata: Optional[Dict]=None,
        parallel_factor: int = 1
) -> Tuple[str, Dict]:
    ...
    ...
    # Исправляем блоки с кодом
    code_block_count = identify_code_blocks(blocks)
    out_meta["block_stats"]["code"] = code_block_count
    indent_blocks(blocks)


    # Исправляем таблицы
    merge_table_blocks(blocks)
    table_count = create_new_tables(blocks)
    out_meta["block_stats"]["table"] = table_count


    for page in blocks:
        for block in page.blocks:
            block.filter_spans(bad_span_ids)
            block.filter_bad_span_types()


    filtered, eq_stats = replace_equations(
        doc,
        blocks,
        block_types,
        texify_model,
        batch_size=int(settings.TEXIFY_BATCH_SIZE * parallel_factor)
    )
    out_meta["block_stats"]["equations"] = eq_stats
    ...
    ...

Шаг 4: Постобработка текста с помощью модели редактора. Соответствующий код:

def convert_single_pdf(
        fname: str,
        model_lst: List,
        max_pages=None,
        metadata: Optional[Dict]=None,
        parallel_factor: int = 1
) -> Tuple[str, Dict]:
    ...
    ...
    # Копирование во избежание изменения исходных данных
    merged_lines = merge_spans(filtered)
    text_blocks = merge_lines(merged_lines, filtered)
    text_blocks = filter_common_titles(text_blocks)
    full_text = get_full_text(text_blocks)


    # Обработка присоединяемых пустых блоков
    full_text = re.sub(r'\n{3,}', '\n\n', full_text)
    full_text = re.sub(r'(\n\s){3,}', '\n\n', full_text)


    # Меняем маркеры списка на -.
    full_text = replace_bullets(full_text)


    # Постобработка текста с помощью модели редактора
    full_text, edit_stats = edit_full_text(
        full_text,
        edit_model,
        batch_size=settings.EDITOR_BATCH_SIZE * parallel_factor
    )
    out_meta["postprocess_stats"] = {"edit": edit_stats}


    return full_text, out_meta

Выводы по Marker’у

Пока что мы лишь описали общий процесс работы Marker. Но нам уже есть, что обсудить — некоторые выводы, которые мы можем сделать на основе полученной информации.

Вывод 1: Анализ макета можно разделить на несколько подзадач. Первая подзадача включает в себя вызов API PyMuPDF для получения блоков страниц.

def ocr_entire_page(page, lang: str, spellchecker: Optional[SpellChecker] = None) -> List[Block]:
    if settings.OCR_ENGINE == "tesseract":
        return ocr_entire_page_tess(page, lang, spellchecker)
    elif settings.OCR_ENGINE == "ocrmypdf":
        return ocr_entire_page_ocrmp(page, lang, spellchecker)
    else:
        raise ValueError(f"Unknown OCR engine {settings.OCR_ENGINE}")




def ocr_entire_page_tess(page, lang: str, spellchecker: Optional[SpellChecker] = None) -> List[Block]:
    try:
        full_tp = page.get_textpage_ocr(flags=settings.TEXT_FLAGS, dpi=settings.OCR_DPI, full=True, language=lang)
        blocks = page.get_text("dict", sort=True, flags=settings.TEXT_FLAGS, textpage=full_tp)["blocks"]
        full_text = page.get_text("text", sort=True, flags=settings.TEXT_FLAGS, textpage=full_tp)


        if len(full_text) == 0:
            return []


        # Проверяем, сработал ли OCR. Если нет, то возвращаем пустой список


        # OCR может не сработать, если была отсканирована пустая страница нечетко отпечатанным текстом
        if detect_bad_ocr(full_text, spellchecker):
            return []
    except RuntimeError:
        return []
    return blocks

Вывод 2: Тонкая настройка (или дообучение) небольших мультимодальных предварительно обученных моделей, таких как LayoutLMv3, для решения конкретных задач может быть весьма полезна. Например, LayoutLMv3 в Marker дообучена таким образом, чтобы позволить модели сегментатора макета определять типы блоков.

def load_layout_model():
    model = LayoutLMv3ForTokenClassification.from_pretrained(
        settings.LAYOUT_MODEL_NAME,
        torch_dtype=settings.MODEL_DTYPE,
    ).to(settings.TORCH_DEVICE_MODEL)


    model.config.id2label = {
        0: "Caption",
        1: "Footnote",
        2: "Formula",
        3: "List-item",
        4: "Page-footer",
        5: "Page-header",
        6: "Picture",
        7: "Section-header",
        8: "Table",
        9: "Text",
        10: "Title"
    }


    model.config.label2id = {v: k for k, v in model.config.id2label.items()}
    return model

Набор данных, использованный для этого дообучения, был взят из открытого набора данных DocLayNet.

Вывод 3: При парсинге PDF-файлов огромное значение имеет число колонок на странице, т.к. от этого зависит порядок чтения документа. Алгоритм Marker также включает дообученую LayoutLMv3, которая представляет из себя модель детектора колонок. Эта модель определяет количество колонок на странице, а затем применяет метод средней точки,

def add_column_counts(doc, doc_blocks, model, batch_size):
    for i in range(0, len(doc_blocks), batch_size):
        batch = range(i, min(i + batch_size, len(doc_blocks)))
        rgb_images = []
        bboxes = []
        words = []
        for pnum in batch:
            page = doc[pnum]
            rgb_image, page_bboxes, page_words = get_inference_data(page, doc_blocks[pnum])
            rgb_images.append(rgb_image)
            bboxes.append(page_bboxes)
            words.append(page_words)


        predictions = batch_inference(rgb_images, bboxes, words, model)
        for pnum, prediction in zip(batch, predictions):
            doc_blocks[pnum].column_count = prediction




def order_blocks(doc, doc_blocks: List[Page], model, batch_size=settings.ORDERER_BATCH_SIZE):
    add_column_counts(doc, doc_blocks, model, batch_size)


    for page_blocks in doc_blocks:
        if page_blocks.column_count > 1:
            # Пересортировка блоков в зависимости от их позиции
            split_pos = page_blocks.x_start + page_blocks.width / 2
            left_blocks = []
            right_blocks = []
            for block in page_blocks.blocks:
                if block.x_start <= split_pos:
                    left_blocks.append(block)
                else:
                    right_blocks.append(block)
            page_blocks.blocks = left_blocks + right_blocks
    return doc_blocks

аналогично моему подходу в статье Advanced RAG 02: Unveiling PDF Parsing.

Вывод 4: Специализированные модели можно обучить обрабатывать математические формулы. Например, Texify, модель от Marker, использует архитектуру Donut. Она была обучена на модели Donut с использованием изображений из LaTex и соответствующих уравнений, взятых из интернета (включая набор данных im2latex). Обучение проводилось на 4-x A6000 в течение примерно двух дней, что соответствует примерно 6 эпохам.

Вывод 5: Модель также можно использовать для постобработки. Основная идея заключается в том, чтобы обучить модель T5 брать почти готовый текст и дорабатывать его, удаляя артефакты, добавляя пробелы и вставляя новые строки.

def load_editing_model():
    if not settings.ENABLE_EDITOR_MODEL:
        return None


    model = T5ForTokenClassification.from_pretrained(
            settings.EDITOR_MODEL_NAME,
            torch_dtype=settings.MODEL_DTYPE,
        ).to(settings.TORCH_DEVICE_MODEL)
    model.eval()


    model.config.label2id = {
        "equal": 0,
        "delete": 1,
        "newline-1": 2,
        "space-1": 3,
    }
    model.config.id2label = {v: k for k, v in   model.config.label2id.items()}
    return model

На сегодняшний это вся информация об обучении постпроцессора и конструировании набора данных, которую мне удалось найти.

Недостатки Marker

Естественно, у Marker есть свои недостатки:

Вместо обучения и тонкой настройки специализированной модели для анализа макета, здесь используется встроенная функция из PyMuPDF. Эффективность такого подхода вызывает сомнения.
Marker не всегда удается распознать таблицы, а также их названия, что уступает в эффективности, например, Nougat (решение на основе небольшой модели без OCR, которое будет подробно представлено в следующей статье). Например, на рисунке 3 представлены результаты распознавания таблицы 3 из статьи "Attention Is All You Need". Слева показана исходная таблица, в середине — результаты с использованием Marker, а справа — результаты Nougat.

Рисунок 3: Сравнение обнаружения и распознавания таблиц, исходная таблица — таблица 3 из статьи "Attention Is All You Need". Изображение автора.

Поддерживаются только языки, похожие на английский. Распарсить PDF-файл на таких языках, как японский и хинди, не получится.

PaperMage

Papermage — это фреймворк с открытым исходным кодом для анализа и обработки визуально насыщенных, структурированных научных документов. Он предоставляет четкие и интуитивно понятные абстракции для представления и манипулирования текстовыми и визуальными элементами в документе.

Papermage объединяет различные модели обработки естественного языка (NLP) и компьютерного зрения (CV) в единый фреймворк. Он предлагает готовые к использованию решения для распространенных сценариев обработки научных документов.

Далее мы расскажем о принципах работы PaperMage и обсудим общий процесс с примерами исходного кода. Затем мы поговорим о выводах, которые можно сделать при рассмотрении PaperMage.

Компоненты

В Papermage можно выделить три основных компонента:

Magelib: Библиотека, содержащая примитивы и методы для представления и манипулирования визуально насыщенными документами в виде мультимодальных структур.
Предикторы: Реализация, объединяющая различные современные модели анализа научных документов в единый интерфейс. Это возможно, даже не смотря на то, что отдельные модели могут быть написаны на разных фреймворках или работать в разных режимах.
Рецепты: Фреймворк предлагает хорошо протестированные комбинации отдельных модулей, часто одномодальных, образующих сложные и расширяемые мультимодальные конвейеры, если можно так выразиться, “под ключ”. Эти комбинации называются рецептами (Recipes).

Базовые классы данных

Magelib предоставляет три базовых класса данных для представления основных элементов визуально насыщенных структурированных документов: Document, Layers (слои) и Entities (сущности).

Документ и слои

На рисунке 4 показано, как PaperMage создает и представляет документы.

Рисунок 4: Как PaperMage создает и представляет документы. Источник: PaperMage.

После того, как с помощью различных алгоритмов и моделей извлечена структура документа, PaperMage концептуализирует ее как слой аннотаций, используемый для хранения как текстовой, так и визуальной информации.

Чуть позже мы посмотрим на исходный код и проанализируем процесс выполнения функции recipe.run().

Сущности

Как показано на рисунке 5, сущность представляет собой единицу мультимодального контента.

Рисунок 5: Сущность PaperMage. Источник: PaperMage.

Но что нам делать с прерывистыми элементами в документе, такими как, например, предложения, которые охватывают целые колонки или даже страницы, или, предположим, обрываются плавающими графиками или сносками?

PaperMage's использует две переменные-члена: спаны (spans) и боксы (boxes). Как показано на рисунке 5, спаны определяют текст предложения среди всех символов, а боксы отражают его визуальные координаты на странице. Такой подход обеспечивает большую гибкость, позволяя учитывать даже незначительные различия в макете.

Кроме того, мы имеем возможность обращаться к сущностям различными способами, как показано на рисунке 6.

Рисунок 6: Различные способы доступа к сущностям. Источник: PaperMage.

Чтобы лучше понять работу Papermage, мы начнем с конкретного примера парсинга PDF-файла и по ходу дела будем углубляться в суть процесса.

Общий анализ процесса и кода

Тестовый код выглядит следующим образом:

from papermage.recipes import CoreRecipe

core_recipe = CoreRecipe()

doc = core_recipe.run("YOUR_PDF_PATH")

Первым делом core_recipe = CoreRecipe() войдет в конструктор класса CoreRecipe, где произойдет инициализация связанных библиотек и моделей.

class CoreRecipe(Recipe):
    def __init__(
        self,
        ivila_predictor_path: str = "allenai/ivila-row-layoutlm-finetuned-s2vl-v2",
        bio_roberta_predictor_path: str = "allenai/vila-roberta-large-s2vl-internal",
        svm_word_predictor_path: str = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/mmda/models/svm_word_predictor.tar.gz",
        dpi: int = 72,
    ):
        self.logger = logging.getLogger(self.__class__.__name__)
        self.dpi = dpi


        self.logger.info("Instantiating recipe...")
        self.parser = PDFPlumberParser()
        self.rasterizer = PDF2ImageRasterizer()


        # with warnings.catch_warnings():
        #     warnings.simplefilter("ignore")
        #     self.word_predictor = SVMWordPredictor.from_path(svm_word_predictor_path)


        self.publaynet_block_predictor = LPEffDetPubLayNetBlockPredictor.from_pretrained()
        self.ivila_predictor = IVILATokenClassificationPredictor.from_pretrained(ivila_predictor_path)
        self.sent_predictor = PysbdSentencePredictor()
        self.logger.info("Finished instantiating recipe")

Поскольку class Recipe является родительским классом CoreRecipe, функция core_recipe.run() перейдет в Recipe::run().

class Recipe:
    @abstractmethod
    def run(self, input: Any) -> Document:
        if isinstance(input, Path):
            if input.suffix == ".pdf":
                return self.from_pdf(pdf=input)
            if input.suffix == ".json":
                return self.from_json(doc=input)


            raise NotImplementedError("Filetype not yet supported.")


        if isinstance(input, Document):
            return self.from_doc(doc=input)


        if isinstance(input, str):
            if os.path.exists(input):
                input = Path(input)
                return self.run(input=input)
            else:
                return self.from_str(text=input)


        raise NotImplementedError("Document input not yet supported.")

Затем он дойдет до class CoreRecipe:: from_pdf() и class CoreRecipe:: from_doc():

class CoreRecipe(Recipe):
    ...
    ...
    def from_pdf(self, pdf: Path) -> Document:
        self.logger.info("Parsing document...")
        doc = self.parser.parse(input_pdf_path=pdf)


        self.logger.info("Rasterizing document...")
        images = self.rasterizer.rasterize(input_pdf_path=pdf, dpi=self.dpi)
        doc.annotate_images(images=list(images))
        self.rasterizer.attach_images(images=images, doc=doc)
        return self.from_doc(doc=doc)


    def from_doc(self, doc: Document) -> Document:
        # self.logger.info("Predicting words...")
        # words = self.word_predictor.predict(doc=doc)
        # doc.annotate_layer(name=WordsFieldName, entities=words)


        self.logger.info("Predicting sentences...")
        sentences = self.sent_predictor.predict(doc=doc)
        doc.annotate_layer(name=SentencesFieldName, entities=sentences)


        self.logger.info("Predicting blocks...")
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            blocks = self.publaynet_block_predictor.predict(doc=doc)
        doc.annotate_layer(name=BlocksFieldName, entities=blocks)


        self.logger.info("Predicting figures and tables...")
        figures = []
        tables = []
        for block in blocks:
            if block.metadata.type == "Figure":
                figure = Entity(boxes=block.boxes)
                figures.append(figure)
            elif block.metadata.type == "Table":
                table = Entity(boxes=block.boxes)
                tables.append(table)
        doc.annotate_layer(name=FiguresFieldName, entities=figures)
        doc.annotate_layer(name=TablesFieldName, entities=tables)


        # self.logger.info("Predicting vila...")
        vila_entities = self.ivila_predictor.predict(doc=doc)
        doc.annotate_layer(name="vila_entities", entities=vila_entities)


        for entity in vila_entities:
            entity.boxes = [
                Box.create_enclosing_box(
                    [b for t in doc.intersect_by_span(entity, name=TokensFieldName) for b in t.boxes]
                )
            ]
            # entity.text = make_text(entity=entity, document=doc)
        preds = group_by(entities=vila_entities, metadata_field="label", metadata_values_map=VILA_LABELS_MAP)
        doc.annotate(*preds)
        return doc

Общий процесс показан на рисунке 7:

Рисунок 7: Общий процесс работы PaperMage; стрелки без меток обозначают операцию слияния, также известную как функция аннотирования в PaperMage. Изображение автора.

На рисунке 7 мы можем увидеть, что процесс обработки PaperMage также представляет из себя конвейерный подход.

Первоначально выполняется анализ макета с помощью библиотеки PDFPlumber. Затем подключаются профессиональные алгоритмы и модели для анализа других объектов на странице, основываясь на результатах анализа макета. Сюда входят предложения, рисунки, таблицы, заголовки и так далее.

Далее мы сосредоточим наше внимание на трех важных процессах:

Разбиение на предложения
Анализ структуры макета
Анализ логической структуры.

Разбиение на предложения

Для разделения предложений используется PySBD — пакет Python для определения границ предложений на основе системы правил.

На вход подается последовательность лексем. На выходе мы получаем спан каждого предложения.

[
Unannotated Entity: {'spans': [[0, 212]]}, 
Unannotated Entity: {'spans': [[212, 367]]},  
…
]

Анализ структуры макета

Для анализа структуры макета страницы используется модель LPEffDetPubLayNetBlockPredictor. Это мощная модель обнаружения объектов на основе глубокого обучения, предоставляемая LayoutParser. Ее основная задача — сегментировать документ на области визуальных блоков.

На вход подается изображение страницы, обозначаемое как doc.images. На выходе мы получаем объект box и соответствующий тип для каждого блока. Бокс включает в себя координату X левой верхней вершины, координату Y левой верхней вершины, ширину, высоту и номер страницы.

[
Unannotated Entity: {'boxes': [[0.5179840190298606, 0.752760137345049, 0.3682081491355128, 0.15176369855069774, 0]], 'metadata': {'type': 'Text'}}, 
Unannotated Entity: {'boxes': [[0.5145780320135539, 0.5080924136055337, 0.3675624668198144, 0.23725746136663078, 0]], 'metadata': {'type': 'Text'}}, 
…
]

Анализ логической структуры

Для анализа логической структуры документа используется модель IVILATokenClassificationPredictor. Она разделяет документ на такие организационные единицы, как заголовок, аннотация, основная часть, сноски, подписи и т.д.

В качестве исходных данных используются данные на уровне страницы, передаваемые в виде словаря.

{
        'words': ['word1', 'word2', ...],
        'bbox': [[x1, y1, x2, y2], [x1, y1, x2, y2], ...],
        'block_ids': [0, 0, 0, 1 ...],
        'line_ids': [0, 1, 1, 2 ...],
        'labels': [0, 0, 0, 1 ...], # could be empty
    }

Выходные данные — спан каждой сущности.

[
Unannotated Entity: {'spans': [[0, 80]], 'metadata': {'label': 'Title'}}, 
Unannotated Entity: {'spans': [[81, 157]], 'metadata': {'label': 'Author'}}, 
Unannotated Entity: {'spans': [[158, 215]], 'metadata': {'label': 'Paragraph'}}, 
...
]

Размышления и выводы о PaperMage

Абстракция парсинга PDF

Абстракция, предложенная PaperMage для задачи парсинга PDF, является достаточно эффективной. Она предполагает разделение всего PDF на такие типы, как doc, layer и entities, что облегчает классификацию элементов и управление ими.

Масштабируемость

PaperMage разработала фреймворк, который легко расширяется, что упрощает последующую разработку.

Например, чтобы добавить пользовательский предиктор, нам достаточно наследоваться от базового класса BasePredictor и переопределить функцию _predict().

from .base_predictor import BasePredictor


class YOUR_NEW_Predictor(BasePredictor):
    ...
    ...
    def _predict(self, doc: Document) -> List[YOUR_RET_TYPE]:
    ...
    ...

Параллелизм

Рисунок 7 показывает, что у PaperMage есть потенциал для улучшения за счет распараллеливания, что является вполне целесообразным направлением для оптимизации.

Хотя текущая версия PaperMage не содержит кода, связанного с параллелизмом, добавление логики параллельной обработки может значительно повысить эффективность парсинга PDF-файлов.

Unstructured

Unstructured — это опенсорсный инструмент предварительной обработки неструктурированных данных. В предыдущей статье мы уже описали в общих чертах процесс его работы.

Теперь же мы поговорим о выводах, которые были сделаны при рассмотрении фреймворка unstructured, в частности о том, как он может помочь нам в разработке собственного инструмента для парсинга PDF.

Об анализе макета

Анализ макета в unstructured производится очень скрупулезно.

Если мы зададим strategy='hi_res', то для анализа макета будут использоваться такие модели, как YOLOX или detectron2. Для улучшения обнаружения они сочетаются с инструментом PDFMiner. Результаты обоих методов объединяются для получения окончательного макета, как показано на рисунке 8.

Рисунок 8: Процесс парсинга PDF со стратегией='hi_res' в unstructured. Изображение автора.

На рисунках 9 и 10 показаны визуализации результатов анализа макета 16-й страницы документа BERT; рамки на рисунке представляют границы каждой области. Результаты модели обнаружения объектов, показанные на рисунке 9, являются более точными. Таблицы и изображения в данном случае лучше интегрированы в структуру документа. Результаты обнаружения PDFMiner, показанные на рисунке 10, наоборот разделяют содержимое таблиц и изображений.

Рисунок 9: Визуализация результатов модели обнаружения объектов (inferred_layout) для 16-й страницы документа BERT, рамки представляют собой границы каждой области. Скриншот автора.

Рисунок 10: Визуализация результатов обнаружения PDFMiner (extracted_layout) для 16-й страницы документа BERT, рамки представляют собой границы каждой области. Скриншот автора.

Код, отвечающий за слияния макетов, выглядит следующим образом: он содержит двойной цикл, который оценивает связь между каждой областью обнаруженной с помощью PDFMiner (extracted_layout) и результатом, полученным от модели обнаружения объектов (inferred_layout), а затем решает, нужно ли их объединять.

def merge_inferred_layout_with_extracted_layout(
    inferred_layout: Collection[LayoutElement],
    extracted_layout: Collection[TextRegion],
    page_image_size: tuple,
    same_region_threshold: float = inference_config.LAYOUT_SAME_REGION_THRESHOLD,
    subregion_threshold: float = inference_config.LAYOUT_SUBREGION_THRESHOLD,
) -> List[LayoutElement]:
    """Merge two layouts to produce a single layout."""
    extracted_elements_to_add: List[TextRegion] = []
    inferred_regions_to_remove = []
    w, h = page_image_size
    full_page_region = Rectangle(0, 0, w, h)
    for extracted_region in extracted_layout:
        extracted_is_image = isinstance(extracted_region, ImageTextRegion)
        if extracted_is_image:
            # Для наших целей мы пропустим извлеченные изображения, у нас нет текста на них, и с ними
            # обычно трудно получить хорошие ограничительные рамки для текста.


            is_full_page_image = region_bounding_boxes_are_almost_the_same(
                extracted_region.bbox,
                full_page_region,
                FULL_PAGE_REGION_THRESHOLD,
            )


            if is_full_page_image:
                continue
        region_matched = False
        for inferred_region in inferred_layout:
            if inferred_region.source in CHIPPER_VERSIONS:
                continue
            ...
            ...

О кастомизации

Unstructured предоставляет множество промежуточных результатов, которые можно легко кастомизировать.

В предыдущей статье мы рассмотрели три проблемы, связанные с данными, получемыми от unstructed:

Парсинг таблиц
Перестановка обнаруженных блоков, особенно в PDF-файлах с двумя колонками
Извлечение многоуровневых заголовков

Последние две проблемы можно решить, изменив промежуточную структуру. В качестве примера на рисунке 11 показан окончательный макет второй страницы документа BERT.

Рисунок 11: Визуализация окончательного макета второй страницы документа BERT. Скриншот автора.

В то же время мы можем легко получить доступные результаты анализа макета:

[


LayoutElement(bbox=Rectangle(x1=851.1539916992188, y1=181.15073777777613, x2=1467.844970703125, y2=587.8204599999975), text='These approaches have been generalized to coarser granularities, such as sentence embed- dings (Kiros et al., 2015; Logeswaran and Lee, 2018) or paragraph embeddings (Le and Mikolov, 2014). To train sentence representations, prior work has used objectives to rank candidate next sentences (Jernite et al., 2017; Logeswaran and Lee, 2018), left-to-right generation of next sen- tence words given a representation of the previous sentence (Kiros et al., 2015), or denoising auto- encoder derived objectives (Hill et al., 2016). ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.9519357085227966, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=196.5296173095703, y1=181.1507377777777, x2=815.468994140625, y2=512.548237777777), text='word based only on its context. Unlike left-to- right language model pre-training, the MLM ob- jective enables the representation to fuse the left and the right context, which allows us to pre- In addi- train a deep bidirectional Transformer. tion to the masked language model, we also use a “next sentence prediction” task that jointly pre- trains text-pair representations. The contributions of our paper are as follows: ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.9517233967781067, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=200.22352600097656, y1=539.1451822222216, x2=825.0242919921875, y2=870.542682222221), text='• We demonstrate the importance of bidirectional pre-training for language representations. Un- like Radford et al. (2018), which uses unidirec- tional language models for pre-training, BERT uses masked language models to enable pre- trained deep bidirectional representations. This is also in contrast to Peters et al. (2018a), which uses a shallow concatenation of independently trained left-to-right and right-to-left LMs. ', source=<Source.YOLOX: 'yolox'>, type='List-item', prob=0.9414362907409668, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=851.8727416992188, y1=599.8257377777753, x2=1468.0499267578125, y2=1420.4982377777742), text='ELMo and its predecessor (Peters et al., 2017, 2018a) generalize traditional word embedding re- search along a different dimension. They extract context-sensitive features from a left-to-right and a right-to-left language model. The contextual rep- resentation of each token is the concatenation of the left-to-right and right-to-left representations. When integrating contextual word embeddings with existing task-speciﬁc architectures, ELMo advances the state of the art for several major NLP benchmarks (Peters et al., 2018a) including ques- tion answering (Rajpurkar et al., 2016), sentiment analysis (Socher et al., 2013), and named entity recognition (Tjong Kim Sang and De Meulder, 2003). Melamud et al. (2016) proposed learning contextual representations through a task to pre- dict a single word from both left and right context using LSTMs. Similar to ELMo, their model is feature-based and not deeply bidirectional. Fedus et al. (2018) shows that the cloze task can be used to improve the robustness of text generation mod- els. ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.938507616519928, image_path=None, parent=None), 




LayoutElement(bbox=Rectangle(x1=199.3734130859375, y1=900.5257377777765, x2=824.69873046875, y2=1156.648237777776), text='• We show that pre-trained representations reduce the need for many heavily-engineered task- speciﬁc architectures. BERT is the ﬁrst ﬁne- tuning based representation model that achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks, outper- forming many task-speciﬁc architectures. ', source=<Source.YOLOX: 'yolox'>, type='List-item', prob=0.9461237788200378, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=195.5695343017578, y1=1185.526123046875, x2=815.9393920898438, y2=1330.3272705078125), text='• BERT advances the state of the art for eleven NLP tasks. The code and pre-trained mod- els are available at https://github.com/ google-research/bert. ', source=<Source.YOLOX: 'yolox'>, type='List-item', prob=0.9213815927505493, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=195.33956909179688, y1=1360.7886962890625, x2=447.47264000000007, y2=1397.038330078125), text='2 Related Work ', source=<Source.YOLOX: 'yolox'>, type='Section-header', prob=0.8663332462310791, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=197.7477264404297, y1=1419.3353271484375, x2=817.3308715820312, y2=1527.54443359375), text='There is a long history of pre-training general lan- guage representations, and we brieﬂy review the most widely-used approaches in this section. ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.928022563457489, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=851.0028686523438, y1=1468.341394166663, x2=1420.4693603515625, y2=1498.6444497222187), text='2.2 Unsupervised Fine-tuning Approaches ', source=<Source.YOLOX: 'yolox'>, type='Section-header', prob=0.8346447348594666, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=853.5444444444446, y1=1526.3701822222185, x2=1470.989990234375, y2=1669.5843488888852), text='As with the feature-based approaches, the ﬁrst works in this direction only pre-trained word em- (Col- bedding parameters from unlabeled text lobert and Weston, 2008). ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.9344717860221863, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=200.00000000000009, y1=1556.2037353515625, x2=799.1743774414062, y2=1588.031982421875), text='2.1 Unsupervised Feature-based Approaches ', source=<Source.YOLOX: 'yolox'>, type='Section-header', prob=0.8317819237709045, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=198.64227294921875, y1=1606.3146266666645, x2=815.2886352539062, y2=2125.895459999998), text='Learning widely applicable representations of words has been an active area of research for decades, including non-neural (Brown et al., 1992; Ando and Zhang, 2005; Blitzer et al., 2006) and neural (Mikolov et al., 2013; Pennington et al., 2014) methods. Pre-trained word embeddings are an integral part of modern NLP systems, of- fering signiﬁcant improvements over embeddings learned from scratch (Turian et al., 2010). To pre- train word embedding vectors, left-to-right lan- guage modeling objectives have been used (Mnih and Hinton, 2009), as well as objectives to dis- criminate correct from incorrect words in left and right context (Mikolov et al., 2013). ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.9450697302818298, image_path=None, parent=None), 


LayoutElement(bbox=Rectangle(x1=853.4905395507812, y1=1681.5868488888855, x2=1467.8729248046875, y2=2125.8954599999965), text='More recently, sentence or document encoders which produce contextual token representations have been pre-trained from unlabeled text and ﬁne-tuned for a supervised downstream task (Dai and Le, 2015; Howard and Ruder, 2018; Radford et al., 2018). The advantage of these approaches is that few parameters need to be learned from scratch. At least partly due to this advantage, OpenAI GPT (Radford et al., 2018) achieved pre- viously state-of-the-art results on many sentence- level tasks from the GLUE benchmark (Wang language model- Left-to-right et al., 2018a). ', source=<Source.YOLOX: 'yolox'>, type='Text', prob=0.9476840496063232, image_path=None, parent=None)


]

Используя приведенную выше информацию, мы можем легко выполнять такие задачи, как сортировка и извлечение многоуровневых заголовков.

Поэтому, разрабатывая собственные инструменты для парсинга PDF, мы должны стремиться сохранить как можно больше полезной промежуточной информации и метаданных.

Об обнаружении и распознавании таблиц

Для обнаружения и распознавания таблиц во фреймворке unstructured используется Table Transformer.

Модель Table Transformer была предложена в статье PubTables-1M: Towards comprehensive table extraction from unstructured documents. В этой статье представлен новый набор данных PubTables-1M, предназначенный для извлечения таблиц из неструктурированных документов и проведения распознавания структуры и функционального анализа таблиц, как показано на рисунке 12.

Рисунок 12: Иллюстрация трех подзадач извлечения таблиц, рассматриваемых в наборе данных PubTables-1M. Источник: PubTables-1M: Towards comprehensive table extraction from unstructured document.

Table Transformer обучен на наборе данных PubTables-1M, основанном на модели DETR, для решения таких задач, как обнаружение таблиц и распознавание их структуры.

Больше информации про обработку таблиц вы найдете в моей предыдущей статье.

Об обнаружении и распознавании формул

Во фреймворке unstructured отсутствует специальный модуль для обнаружения и распознавания формул, что в принципе заметно по посредственным результатам, показанным на рисунке 13.

Рисунок 13: Слева показан результат парсинга абзаца на 6-й странице статьи BERT, включая формулу, выделенную красной рамкой. Справа показан оригинал статьи. Скриншот автора.

Заключение

В этой статье представлен обзор конвейерного подхода к парсингу PDF-файлов. В ней рассматривается этот подход на примере трех фреймворков, использующих этот метод, дается его подробное представление и излагаются выводы, сделанные на его основе.

В итоге,

Хотя у Marker есть несколько недостатков, это легкий и быстрый инструмент.
Хотя PaperMage в первую очередь предназначен для работы с научными документами, его исключительная масштабируемость служит хорошей отправной точкой для дальнейшего развития.
Unstructured — это комплексный конвейерный фреймворк для парсинга PDF. Его преимущества заключаются в детальном анализе макета и широких возможностях кастомизации.

В целом, конвейерный подход к парсингу PDF-файлов является легко интерпретируемым и настраиваемым, что делает его широко используемым методом. Однако его эффективность во многом зависит от производительности каждой модели или алгоритма, используемого в процессе. Поэтому обучающие данные и структура каждой модели должны быть тщательно продуманы.

Материал подготовлен в рамках практического онлайн-курса "MLOps".

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14 авг. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER
26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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18.07.24 20:13 austinagastya

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27.08.24 12:50 James889900

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27.08.24 13:06 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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19.11.24 03:06 [email protected]

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19.11.24 03:07 [email protected]

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21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

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26.11.24 21:59 [email protected]

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26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

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04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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27.12.24 00:21 swiftdream

I lost about $475,000.00 USD to a fake cryptocurrency trading platform a few weeks back after I got lured into the trading platform with the intent of earning a 15% profit daily trading on the platform. It was a hell of a time for me as I could hardly pay my bills and got me ruined financially. I had to confide in a close friend of mine who then introduced me to this crypto recovery team with the best recovery SWIFTDREAM i contacted them and they were able to completely recover my stolen digital assets with ease. Their service was superb, and my problems were solved in swift action, It only took them 48 hours to investigate and track down those scammers and my funds were returned to me. I strongly recommend this team to anyone going through a similar situation with their investment or fund theft to look up this team for the best appropriate solution to avoid losing huge funds to these scammers. Send complaint to Email: info [email protected]
31.12.24 04:53 Annette_Phillips

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04.01.25 04:56 florencestella

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04.01.25 04:57 florencestella

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06.01.25 19:09 michaeljordan15

We now live in a world where most business transactions are conducted through Bitcoin and cryptocurrency. With the rapid growth of digital currencies, everyone seems eager to get involved in Bitcoin and cryptocurrency investments. This surge in interest has unfortunately led to the rise of many fraudulent platforms designed to exploit unsuspecting individuals. People are often promised massive profits, only to lose huge sums of money when they realize the platform they invested in was a scam. contact with WhatsApp: +1 (443) 859 - 2886 Email @ digitaltechguard.com Telegram: digitaltechguardrecovery.com website link:: https://digitaltechguard.com This was exactly what happened to me five months ago. I was excited about the opportunity to invest in Bitcoin, hoping to earn a steady return of 20%. I found a platform that seemed legitimate and made my investment, eagerly anticipating the day when I would be able to withdraw my earnings. When the withdrawal day arrived, however, I encountered an issue. My bank account was not credited, despite seeing my balance and the supposed profits in my account on the platform. At first, I assumed it was just a technical glitch. I thought, "Maybe it’s a delay in the system, and everything will be sorted out soon." However, when I tried to contact customer support, the line was either disconnected or completely unresponsive. My doubts started to grow, but I wanted to give them the benefit of the doubt and waited throughout the day to see if the situation would resolve itself. But by the end of the day, I realized something was terribly wrong. I had been swindled, and my hard-earned money was gone. The realization hit me hard. I had fallen victim to one of the many fraudulent Bitcoin platforms that promise high returns and disappear once they have your money. I knew I had to act quickly to try and recover what I had lost. I started searching online for any possible solutions, reading reviews and recommendations from others who had faced similar situations. That’s when I came across many positive reviews about Digital Tech Guard Recovery. After reading about their success stories, I decided to reach out and use their services. I can honestly say that Digital Tech Guard Recovery exceeded all my expectations. Their team was professional, efficient, and transparent throughout the process. Within a short time, they helped me recover a significant portion of my lost funds, which I thought was impossible. I am incredibly grateful to Digital Tech Guard Recovery for their dedication and expertise in helping me get my money back. If you’ve been scammed like I was, don’t lose hope. There are solutions, and Digital Tech Guard Recovery is truly one of the best. Thank you, Digital Tech Guard Recovery! You guys are the best. Good luck to everyone trying to navigate this challenging space. Stay safe.
18.01.25 12:41 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
18.01.25 12:41 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]