Accelerating and Compressing LSTM Based Model for Online Handwritten Chinese Character Recognition

With the development of deep learning tools, the online handwritten Chinese character recognition (HCCR) performance has been greatly improved by using deep neural networks (DNNs) especially for long short-term memory (LSTM). However, DNNs suffer from large consumption of computation and storage resources, which may cause problems for service providers, such as server pressure, longer service latency and higher energy consumption. To solve these problems, we propose a framework that combines singular value decomposition (SVD) and adaptive drop weight (ADW) to accelerate and compress LSTM based models. We first build an LSTM based model that achieves an accuracy of 97.83% on the ICDAR2013 online HCCR competition dataset. After restructuring the model with SVD and ADW, it can reduce the FLOPs (floating point operations per second) of the forward process by approximately 10 times and compress the model with 1/30 of the original size with only a 0.5% decrease in accuracy. Finally, integrated with our efficient forward implementation, the recognition of an online character requires only 2.7 ms in average on a CPU with a single thread, while requiring only 0.45 MB for model storage.