Empirical Analysis of Classification Approaches for Indian Language Processing

In the realm of pattern recognition, decoding handwritten characters remains a focal point, drawing enduring interest from researchers across various domains. Optical Character Recognition (OCR) stands as a pivotal technique in image processing, extracting text, and identifying language from digital images. While OCR has made strides in Arabic and Chinese, Indian scripts lack comprehensive exploration. This paper addresses this gap, delving into OCR methodologies for Indian scripts and multiple languages. By scrutinizing each language, it unravels the intricacies and challenges of character recognition. The study offers a comprehensive view of OCR techniques’ adaptability and effectiveness in diverse linguistic contexts. This research marks a significant contribution by extensively exploring OCR’s application to Indian scripts and their languages, filling a notable void in current understanding within this domain.