Devanagari Character Recognition: A Comprehensive Literature Review

Abstract

The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. Recognizing handwritten Devanagari characters poses significant challenges due to their complexity and handwriting variability. This literature review examines the evolution of handwritten Devanagari character recognition (HDCR), exploring early template matching and feature extraction methods that struggled with the script’s intricacy. Advances introduced structural and statistical techniques, improving accuracy by analyzing geometric properties and patterns. The advent of machine learning, particularly deep learning, revolutionized HDCR with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), significantly enhancing performance. Hybrid approaches that combine multiple techniques have shown promising results, balancing accuracy and computational complexity. Challenges remain, including handwriting variability, noise, and the need for real-time performance. The lack of large, diverse datasets for training and evaluation is a significant hurdle. This review highlights efforts to create annotated datasets and benchmarks, providing a comprehensive overview of HDCR methodologies, strengths, limitations, and future research directions. These insights aim to advance HDCR, contributing to more accurate and efficient recognition systems and enhancing digital text processing for linguistic, educational, and archival purposes.