HWRCNet: Handwritten Word Recognition in JPEG Compressed Domain using CNN-BiLSTM Network

The handwritten word recognition from images using deep learning is an active research area with promising performance. It practical scenario, it might be required to process the handwritten images in the compressed domain due to due to security reasons. However, the utilization of deep learning is still very limited for the processing of compressed images. Motivated by the need of processing document images in the compressed domain using recent developments in deep learning, we propose a HWRCNet model for handwritten word recognition in JPEG compressed domain. The proposed model combines the Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) based Recurrent Neural Network (RNN). Basically, we train the model using compressed domain images and observe a very appealing performance with 89.05% word recognition accuracy and 13.37% character error rate.