A Review of Optical Character Recognition for Handwritten Hindi Text

Abstract: Handwritten text recognition has long been a challenging area within the field of pattern recognition, especially for complex scripts such as Hindi written in the Devanagari script. Unlike printed text, handwritten Hindi exhibits significant variability in writing styles, stroke order, character shapes, and spacing, making Optical Character Recognition (OCR) a difficult problem. Over the past decade, advancements in machine learning, deep learning, and image processing techniques have significantly improved the performance of OCR systems for handwritten scripts.

This paper presents a detailed review of existing approaches for handwritten Hindi text recognition. It explores traditional methods based on feature extraction and classification, as well as modern deep learning approaches such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid architectures. The study also examines preprocessing techniques, segmentation challenges, and benchmark datasets used in the domain. A structured classification of OCR systems is proposed based on methodology and level of automation.

Further, a comparative analysis is provided considering recognition accuracy, robustness, dataset dependency, and computational complexity. The review highlights that while deep learning models have achieved promising results, challenges such as lack of large annotated datasets, variability in handwriting, and segmentation errors still persist. The paper concludes by identifying research gaps and suggesting future directions for building more accurate, scalable, and real-time OCR systems for handwritten Hindi text.

Keywords: Optical Character Recognition, Handwritten Hindi, Devanagari Script, Deep Learning, CNN, RNN, Image Processing, Pattern Recognition, NLP