Breaking Barriers in Text Analysis: Leveraging Lightweight OCR and Innovative Technologies for Efficient Text Analysis

Text detection and classification play vital roles in various applications such as document analysis, scene text recognition, and image-based information retrieval. In recent years, deep learning approaches combined with Optical Character Recognition (OCR) engines have shown promising results in text-related tasks. This research study investigates text detection and classification using popular OCR libraries, including Tesseract, Keras, Easy OCR, and Paddle OCR A comprehensive evaluation is conducted on a diverse dataset encompassing different text types, languages, and image qualities. Each OCR model’s performance is measured in terms of accuracy, precision, recall, and F1 score. The paper provides sample inputs and corresponding outputs for each model, allowing users to mention the strengths and limitations of each OCR approach. This study aims to assist the practitioners and researchers in selecting the most suitable OCR model for their specific text-related applications. The research findings demonstrate the potential and versatility of deep learning-based OCR solutions for text detection and classification tasks, paving the way for further advancements in this domain.