OPTIMIZED OCR APPROACHES FOR ACCURATE TEXT EXTRACTION IN LEGAL AND FINANCIAL DOCUMENT AUTOMATION

Optical Character Recognition (OCR) is a fundamental technology used to digitize and extract text from printed and handwritten documents, playing a crucial role in legal and financial domains.Traditional OCR techniques, such as bounding box analysis, typically rely on rectangular text segmentation but face significant limitations when processing complex layouts, varying text orientations, and handwritten elements.These challenges highlight the need for a more adaptive and robust approach to enable efficient and accurate text extraction in document-intensive industries.The proposed method integrates advanced natural language processing (NLP)-based post-processing to enhance contextual accuracy and significantly reduce recognition errors.Unlike conventional bounding box analysis, the system dynamically adjusts to diverse text structures, making it particularly effective for processing multi-column legal documents, financial statements, and tabular data.This adaptability ensures precise extraction from a wide range of document formats, thus streamlining workflow automation in the legal and financial sectors.Performance evaluations demonstrate that the proposed OCR system outperforms traditional techniques in terms of recognition accuracy, processing speed, and scalability.By addressing the shortcomings of existing methods, this innovation offers a transformative solution for legal and financial institutions seeking improved efficiency, accuracy, and automation in their document handling processes.