A HYBRID DEEP LEARNING MODEL FOR HANDWRITTEN BANGLA AND ENGLISH DIGIT RECOGNITION

Abstract

Convolutional Neural Network (CNN) is widely used for handwritten Bangla and English digit recognition. The problem with the conventional CNN is that it has an intricate and time-consuming convolution process to create a feature map and it requires a lot of computation time during the classification phase. This work primarily proposes substituting the convolution kernels of the classic CNN model with dilated convolution kernels to address the limitations in handwritten digit recognition. Although the computation time decreases, the Dilated CNN model’s feature extraction part still performs inefficiently due to information loss, which lowers accuracy. Observing these above-mentioned problems, a Hybrid Dilated CNN (HDC) model is proposed by using dilated convolution kernels with different dilation rates to eliminate the detail loss problem of the Dilated CNN model. The proposed Hybrid Dilated CNN (HDC) model can achieve larger receptive fields and can extract distant features. Finally, the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are applied as a classifier to increase the digit classification accuracy. The BanglaLekha-Isolated dataset of Bangla digits is used to verify the proposed methods and demonstrate that under the same conditions, the accuracy of the Dilated CNN, HDC, and the proposed HDC with KNN are 95.85%, 96.72%, and 96.80%, respectively. The proposed HDC model with KNN shows 100% accuracy in the case of the MNIST dataset of English digits. The HDC – KNN model's effectiveness in recognizing digits of both Bangla and English language is perfectly demonstrated by the values of Precision, Recall, and F1 Score parameters. More evidence of the HDC – KNN model's superiority comes from the values of TPR and FPR parameters that are calculated for each of the digit classes in the case of both languages. These experimental findings suggest that integrating the HDC with the KNN algorithm improves the effectiveness of recognizing handwritten Bangla and English digits.