A Lightweight Script-Aware OCR Framework for Multilingual Text Recognition in Real-World Images

Abstract

English, Hindi and Marathi Multilingual optical character recognition (OCR) is challenging where the text is scanned on real-life images due to blur, uneven lighting, background noise, and mixed script writing. The paper introduces a lightweight script-aware OCR system that enhances recognition accuracy by using coordinated preprocessing, connected-component-based word localization, script routing, and confidence-based re-recognition instead of retraining the entire system. The pipeline consists of bilateral filtering, fast non-local means denoising, contrast-limited adaptive histogram equalization (CLAHE), adaptive thresholding, connected component analysis, script identification, and dual-pass Tesseract recognition. The algorithm is aimed at low-resource multilingual document comprehension in which interpretability, tunability, and relatively low computational efficiency are of significance. Experiments using a 5000 image corpus indicate a recognition accuracy of 93.0 on average and language-wise accuracies of 95% in English, 92% in Hindi and 90% in Marathi. The error rate in the characters within English, Hindi and Marathi were 4.2, 6.5 and 7.1 respectively and the script identification stage was 0.94 with a recall 0.93 and F1-score 0.935. The analysis of ablation reveals that adaptive thresholding and script identification are the most significant ones. The findings suggest that a well-designed, script-sensitive pipeline offers both a viable and technically rational solution to multilingual OCR of realistic images.