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PP-DocBee - A multimodal large model for document and image understanding launched by Baidu PaddlePaddle

PP-DocBee is a multimodal large-scale model developed by the Baidu PaddlePaddle team, focusing on document image understanding. Based on the ViT+MLP+LLM architecture, it has powerful Chinese document parsing capabilities and can efficiently process text, tables, etc.

What is PP-DocBee?

PP-DocBee is a multimodal large-scale model developed by the Baidu PaddlePaddle team, focusing on document image understanding. Based on the ViT+MLP+LLM architecture, it possesses powerful Chinese document parsing capabilities, efficiently processing various document types such as text, tables, and charts. PP-DocBee has achieved state-of-the-art (SOTA) performance in authoritative academic evaluations for models with similar parameter sizes and demonstrates excellent performance in internal business Chinese scenarios. PP-DocBee's inference performance has been optimized for faster response times and maintains high-quality output. PP-DocBee is suitable for scenarios such as document question answering and complex document parsing, supports multiple deployment methods, and provides an efficient and intelligent solution for document processing.

Main functions of PP-DocBee

  • Document content comprehensionPP-DocBee accurately recognizes and understands text, tables, charts, and other elements in document images, and supports multimodal input, including text and images.
  • Document Q&A: Ask questions based on the document content and generate accurate answers by combining the information in the document.
  • Structured information extractionTransform information in documents (such as tables and charts) into structured data to facilitate further analysis and processing.

The technical principles of PP-DocBee

  • Architecture DesignBased on the architecture of ViT (Visual Transformer) + MLP (Multilayer Perceptron) + LLM (Large Language Model), it combines the advantages of visual and language models to achieve end-to-end document understanding.
  • Data synthesis and preprocessingTo address the limitations of Chinese document comprehension, an intelligent document data production solution was designed, including combining a small OCR model with a large LLM model and generating image data based on a rendering engine. A larger resize threshold was set during training, and images were proportionally enlarged during inference to obtain more comprehensive visual features.
  • Training optimizationThis approach involves mixing various document understanding data sources (such as general VQA, OCR, charts, mathematical reasoning, etc.) and setting a data matching mechanism to balance the quantitative differences between different datasets. Based on OCR post-processing assistance, the text results recognized by OCR are used as prior information to improve the model's ability to understand images with clear text.

PP-DocBee project address

Application scenarios of PP-DocBee

  • FinanceIt analyzes financial statements, invoices, and other documents to extract key data, assisting in financial analysis and auditing.
  • Legal field:Process contracts, regulations, and other documents, quickly locate clauses, and support legal compliance reviews.
  • academic fieldExtract textual and graphical information from papers to aid in literature retrieval and research analysis.
  • Enterprise document managementExtract and structure internal document content to optimize document retrieval and management processes.
  • Education: Analyze textbooks and test papers to assist in the development of teaching resources and personalized learning.
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