Performance Evaluation of Deep Learning Networks on Printed Odia Characters

Deep machine learning includes a series of layers to mimic the working of the human brain for taking a decision. Deep learning networks have shown good results in character recognition in the past. This paper evaluates the performance of different deep learning networks like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) based recurrent neural network and Convolutional LSTM on printed Odia characters. The Odia character database contains more than 24,000 images of printed Odia characters (including simple as well as complex characters) out of which 23,857 nos. of images are chosen for this work. Besides these three, a nested Convolutional neural network model is developed for different categories of printed character image groups which are formed based on their writing style. Here, in this study, the nested model is showing the best results in terms of error rate, accuracy and no. of epochs in comparison to the other three. Different pre-processing steps like binarization, size-normalization, blurring, interpolation, etc. are involved before passing the images to the deep neural networks to increase the recognition accuracy.