AI-Enhanced Multi-OCR Framework with NLP Post-processing for Improved Handwritten Text Recognition and Analysis

Handwritten text recognition remains challenging due to diverse handwriting styles, image quality variations, and limitations inherent in single Optical Character Recognition (OCR) tools. This study introduces a novel AI-enhanced OCR framework that combines multiple OCR engines with advanced Natural Language Processing (NLP) post-processing techniques, including paraphrasing, summarization and sentiment analysis. The multi-OCR approach strategically leverages the strengths of each OCR engine to optimize initial recognition accuracy. Subsequent NLP refinement significantly reduces OCR-induced errors, enhances readability, and provides contextual clarity. Comprehensive evaluations on synthetic and real-world handwritten datasets demonstrate marked improvements, evidenced by reductions in Word Error Rate (WER) and enhancements in precision, recall, and F1-score. Furthermore, an interactive interface developed using Gradio facilitates real-time processing and intuitive visualization of OCR and NLP outcomes, underscoring the practical applicability of the proposed system. This research provides a robust, integrative solution for handwritten text digitization and analysis, addressing critical gaps in existing OCR technologies.