The project offers a detailed technical overview, including PDF processing and OCR, text processing pipeline, LLM integration, token management, quality assessment, and logging and error handling. It also provides a guide for installation, usage, and configuration. The system generates several output files, including raw OCR output and LLM corrected output. Despite its capabilities, the system's performance is heavily dependent on the quality of the LLM used, and processing large documents can be time-consuming and resource-intensive.
Key takeaways:
- The LLM-Aided OCR Project is an advanced system that improves the quality of Optical Character Recognition (OCR) output using natural language processing techniques and large language models (LLMs).
- The system includes features such as PDF to image conversion, OCR using Tesseract, advanced error correction using LLMs, smart text chunking, markdown formatting, and quality assessment of the final output.
- The project supports both local LLMs and cloud-based API providers like OpenAI and Anthropic, and it uses asynchronous processing for improved performance.
- The system is configurable and customizable, with the ability to suppress headers and page numbers, manage tokens dynamically, and assess output quality. It also provides detailed logging for process tracking and debugging.