Deep Diving into Strategies of Document AI: 30 day LLM Transformation Guide.

In the Part 1 of this series, we introduced the key problems with extracting data from PDFs and why it is a valuable problem.

In Part 2 of our series, we delve into the strategies for effective data extraction from PDFs. This process involves multiple stages, each addressing different aspects of the document:

1. Document Classification: The initial step involves classifying the document type, such as memos, invoices, or manuals. This helps in determining the appropriate extraction technique.
2. Structure Identification: This stage focuses on detecting tables, charts, and other specific structures within the document. Understanding the layout is crucial for accurate data extraction.
3. Data Extraction: Once the structures are identified, the next step is to extract them. For tables, this means discerning rows and columns correctly; for charts, it involves identifying axes and data points.
4. Text Conversion: Smaller components, like individual table cells, are converted into text using OCR tools or non-OCR solutions. This step transforms visual data into a text format.
5. Semantic Extraction: The final stage is about extracting the meaning from the text, enabling language operations like question answering and analysis.

These tasks range from computer vision (first three tasks) to hybrid vision-language and purely language tasks, culminating in a comprehensive data extraction process. Strategies for these tasks include:

1. Text-focused Extraction: Tools like PDFMiner or PyMuPDF are effective for extracting plain text. Pairing these with an LLM, such as GPT-4, can interpret the textual content, especially in layout-simple documents.
2. Integrated OCR and Layout Analysis: Proprietary engines like Amazon Textract, Google’s Document AI, and Azure’s AI Document Intelligence, along with newcomers like LlamaParse, provide OCR and layout analysis. These tools, combined with LLM platforms like Bedrock, offer robust solutions, though their accuracy varies across different document types.
3. State-of-the-Art Model Combinations: Utilizing advanced models like Impira’s LayoutLM, Microsoft’s DiT, and Table Transformers (TATR) facilitates detailed structure analysis and extraction. Subsequent OCR processing of individual cells with tools like Easy OCR, Paddle, or Tesseract, followed by an LLM, enhances the accuracy and depth of extraction.
4. OCR-Free Approaches: DocVQA models, like Donut, bypass traditional OCR methods, directly interpreting documents through specialized transformer models. Similarly, DocLLM applies language models directly to structured data like tables.
5. Multimodal LLMs: These models, including GPT-4V, Claude 3 Opus, and newer entrants like Deepseek-VL, Qwen VL, and TextMonkey, are designed to analyze documents and perform a range of language tasks natively, offering a holistic approach to document analysis.

These strategies showcase the diversity of tools and methods available for PDF data extraction, each suited to different types of documents and extraction needs. As we progress, we’ll evaluate these strategies’ effectiveness and their application in various document contexts.