A Comparative Study of Machine Learning and Deep Learning Approaches for Handwritten Sanskrit Recognition

Abstract

Sanskrit holds immense historical, cultural, and scientific value, with many handwritten manuscripts preserving knowledge in philosophy and traditional medicine like Ayurveda. It is essential to digitize these materials to preserve this legacy and provide broader access. Handwritten Sanskrit recognition remains an underexplored yet essential area within Optical Character Recognition (OCR), largely due to the complexity of the script. Elements such as the Shirolekha (headline), compound characters, modifiers, and irregular character boundaries contribute to the difficulty of accurate recognition. This paper presents a comprehensive study of classical and contemporary techniques adopted for Sanskrit and Devanagari script recognition. The study explores machine learning methods such as Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Hidden Markov Models (HMM), alongside modern deep learning models including CNNs, LSTMs, Bidirectional LSTMs, and CapsNet. The analysis shows that while traditional methods work well with limited data, deep learning approaches achieve higher accuracy, like CNN-based architectures, which reach up to 99.65% but require substantially larger datasets and risk overfitting. A key finding is that most existing research focuses on isolated characters, leaving word-level recognition and complex conjuncts largely unaddressed. Persistent issues such as image noise, overlapping characters, and the lack of large-scale Sanskrit word datasets continue to hinder progress. This study highlights existing gaps and proposes future directions, emphasizing the need for hybrid deep learning models, linguistic context via NLP, and benchmark datasets for improved OCR systems.