Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
This work addresses the challenge of handwritten character recognition for the Bangla language, which is incremental as it applies a modified training technique to an existing domain-specific task.
The paper tackles the problem of recognizing handwritten isolated Bangla compound characters by introducing a novel deep learning approach, achieving a recognition accuracy of 90.33% (error rate 9.67%) on the CMATERdb 3.1.3.3 dataset, which sets a new benchmark with nearly a 10% improvement over previous methods.
In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy layer wise training of Deep Neural Network has helped to make significant strides in various pattern recognition problems. We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence. We compare results with those obtained from standard shallow learning methods with predefined features, as well as standard DCNNs. Supervised layerwise trained DCNNs are found to outperform standard shallow learning models such as Support Vector Machines as well as regular DCNNs of similar architecture by achieving error rate of 9.67% thereby setting a new benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%, representing an improvement of nearly 10%.