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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]

My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
19.11.24 03:07 [email protected]

My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
21.11.24 04:14 ronaldandre617

Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
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
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
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

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25.11.24 02:20 briankennedy

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