An Optimized Workflow for Odia Handwritten Character Recognition

Abstract

Classifying handwritten Odia scripts is challenging because of the script’s complex character shapes and the lack of large annotated datasets. Odia is a low-resource language with only limited digital materials, making the development of effective recognition systems important for improving access and ensuring fair digital representation. The present study addresses the classification of handwritten Odia data, including basic characters, digits, and a set of frequently used compound characters. The proposed method combines several preprocessing steps with a lightweight Convolutional Neural Network (CNN), and data augmentation is applied to enrich the training samples and reduce overfitting. To evaluate the approach, four benchmark datasets were used: NITROHCS V1.0 (basic characters), ISI Kolkata (digits), IIT Bhubaneswar (digits and characters), and IIITBOdiaV2 (digits and characters). The model was trained on one dataset and tested on the others to assess adaptability. Additional evaluation was performed on real handwritten data consisting of both characters and digits. The experimental results demonstrate the effectiveness of the CNN model, showing an accuracy that either surpasses or closely matches earlier proposed systems using the same dataset.