Building Compact CNN-DBLSTM Based Character Models for Handwriting Recognition and OCR by Teacher-Student Learning

Character models based on convolutional neural network (CNN) and deep bidirectional long short-term memory (DBLSTM) have achieved high recognition accuracy on various handwriting recognition (HWR) and OCR tasks. To deploy CNN-DBLSTM models in products, it is necessary to reduce the footprint and runtime latency as much as possible. In this paper, we use a teacher-student learning approach to achieve this goal, where a new objective function is proposed to match the extracted CNN feature sequences of the teacher and student models under the guidance of the succeeding LSTM layer. Experimental results on large scale English HWR and OCR tasks show that the learned small student model can achieve about 14.6x footprint reduction and 9.6x speedup without recognition accuracy degradation against the big teacher model.