RATNet: A deep learning model for Bengali handwritten characters recognition

The Bengali language is based on a set of symbols for basic characters, modifiers, compound characters, and numerals. The recognition rates of handwritten basic characters and numerals are very high. However, the recognition rates of compound characters and modifiers are still poor. This might be due to their large class size with huge writing styles, much similarity, and unavailability of sufficient data for deep learning. In fact, there are some compound characters which appear very rare in practice. A proper selection of frequently used characters may reduce class size, and hence improving the accuracy. In this study, we performed a statistics on the frequency of compound characters, we developed two datasets for modifiers and compound characters, and finally we proposed a heterogeneous deep learning model (RATNet) for characters recognition. A statistics was performed on two daily Bengali newspapers, and characters with frequency >= 5% were selected. The handwriting of selected characters was collected from 130 writers of different ages and professions. The performance of RATNet model was evaluated on the proposed datasets and also three other existing datasets (i.e., ISI, CMATERdb, BanglaLekha-Isolated). In addition, the performance of RATNet was also compared with LeNet-5, VGG-16, ResNet-50, and DenseNet-121 models. We selected 87 out of 107 compound characters. The proposed RATNet model outperforms other models providing 99.66%, 99.27%, 98.78%, and 97.70% accuracy, respectively for the recognition of numerals, basic characters, modifiers, and compound characters on the CMATERdb dataset while keeping the number of parameters relatively low likely due to layer heterogeneity.