An Improved Lightweight Framework for Handwritten Chinese Text Recognition Based on CRNN

Abstract

Robots with computer vision and text recognition functions are widely used in industrial production, especially in highly automated factories. However, most robots have an excellent ability to recognize printed characters and show low accuracy in recognition of handwritten characters. Therefore, this paper considers recognizing handwritten text in the intelligent processing of handwritten documents. Its high accuracy prediction results are closely related to the effectiveness of manuscript input, intelligent translation, and intelligent scoring. Handwritten text is more difficult to recognize because it contains sequential information, and the images are more complex than single-character images. This paper proposes a new handwritten Chinese text recognition (HCTR) framework based on existing classical convolutional neural network (CNN) and recurrent neural network (RNN) algorithms. We use a handwritten Chinese text dataset from CASIA-HWDB containing numbers and symbols close to real application scenarios to train the model and compare the performance of various models, such as MobileNetV1 and MobileNetV2, with the proposed model. From the analysis of experimental results, it can be found that the proposed method can achieve higher performance with fewer parameters. In addition, we optimize the dropout rates of input blocks and obtain the best CER of our method is 6.11%.