Handwritten Odia numeral recognition using combined CNN-RNN
Abhishek Das, Mihir Narayan Mohanty · International Journal of Grid and Utility Computing · 2023
Detection and recognition of handwritten characters play a vital role in natural language processing. In this work, authors have taken an approach for Odia handwritten numbers recognition. A little work has been done in Odia numeral recognition. IIT-Bhubaneswar Odia handwritten numerals dataset is considered in this work that contains 5164 images. Deep learning models need a large number of data for training. 1000 images generated through Deep Convolutional Generative Adversarial Network (DCGAN) were added to the dataset to increase its size. Since single models could not perform well in classifying the images, a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) cells are considered for classification. The Adam Optimisation algorithm is used for minimising the error, and to train the network the supervised learning method is used. The proposed deep learning-based hybrid model provided 98.32% recognition accuracy that was found to be competitive with the state-of-art methods.