What is PP-DocBee
PP-DocBee is a multimodal large model focused on document image understanding launched by the Baidu PaddlePaddle team. Based on the ViT+MLP+LLM architecture, it has powerful Chinese document parsing capabilities and can efficiently process multiple types of document content such as text, tables, and charts. PP-DocBee reaches the SOTA level of models with the same parameter count in authoritative academic evaluations and performs excellently in internal business Chinese scenarios. PP-DocBee's inference performance has been optimized, with faster response speeds while maintaining high-quality output. PP-DocBee is suitable for scenarios such as document question answering and complex document parsing, supports multiple deployment methods, and provides efficient and intelligent solutions for document processing.
Main features of PP-DocBee
- Document content understanding: PP-DocBee accurately recognizes and understands elements such as text, tables, and charts in document images, and supports multimodal input, including text and images.
- Document question answering: Ask questions based on document content and generate accurate answers by combining information in the document.
- Structured information extraction: Convert information in documents (such as tables and charts) into structured data for further analysis and processing.
Technical principles of PP-DocBee
- Architecture design: Based on the architecture of ViT (Vision 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 preprocessing: To address the shortcomings in Chinese document understanding, an intelligent production solution for document-type data is designed, including the combination of small OCR models and large LLM models, and the generation of image data based on rendering engines. A larger resize threshold is set during training, and images are enlarged proportionally during inference to obtain more comprehensive visual features.
- Training optimization: Mix various document understanding data (such as general VQA, OCR, charts, mathematical reasoning, etc.), set up a data ratio mechanism, and balance the quantity differences among different datasets. With the assistance of OCR post-processing, the text results recognized by OCR are used as prior information to improve the model's understanding ability on images with clear text.
Project address of PP-DocBee
- GitHub repository：https://github.com/PaddlePaddle/PaddleMIX/tree/develop/deploy/ppdocbee
- Online experience Demo：https://aistudio.baidu.com/application/detail/60135
Application scenarios of PP-DocBee
- Finance field: Parse documents such as financial reports and invoices, extract key data, and assist financial analysis and auditing.
- Legal field:Process documents such as contracts and regulations, quickly locate clauses, and support legal compliance review.
- Academic field: Extract text and chart information from papers to assist literature retrieval and research analysis.
- Enterprise document management: Extract and structure internal document content, and optimize document retrieval and management processes.
- Education field: Parse textbooks and exam papers to assist teaching resource development and personalized learning.