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By: Bhasha Sinha, Shrishti Saxena, Anuva Vashishtha, Aradhna Aggarwal, and Rajesh Yadav
This paper describes an automated legal document processing system that uses modern technologies, such as Large Language Models (LLMs) and Optical Character Recognition (OCR). Designed to streamline legal procedures, the system allows users to submit legal documents in PDF format and uses Tesseract-powered OCR to extract and digitize text while keeping the document’s layout and structure. The digitized text is processed by an LLM that has been fine-tuned using legal datasets to ensure that key legal nuances are retained. The system provides a variety of functionalities, including full-document summarization, clause-specific summarization, and targeted clause extraction, to meet a wide range of legal purposes. With its user-centric design, the React is interface offers an interactive and customizable experience, allowing users to select specific areas for summarization, highlight critical bits, and fine tune results to meet their needs. The results are presented in a structured way to ensure clarity and practical insights. The backend uses FastAPI for easy processing and PyTorch for model implementation, which ensures robustness and scalability. Furthermore, tools like NLTK help with natural language processing tasks like text analysis and structure. To improve user ease, the system incorporates Google Drive for automatic session management, allowing users to review or edit previously processed documents. This system revolutionizes legal document automation by merging cutting-edge technology, such as Tesseract OCR, PyTorch, and finely designed LLMs. It benefits legal professionals by reducing time, increasing efficiency, and ensuring the correctness and context of legal documents. This system is a vital tool for modern legal offices, meeting the growing demand for intelligent and efficient document processing solutions.
Keywords: Artificial Intelligence (AI) in Law, Document Summarization, Large Language Models (LLMs), Legal Document Automation, Legal Technology, Natural Language Processing (NLP), Optical Character Recognition (OCR)
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