English handwriting recognition based on the convolutional neural network

Abstract

Handwriting recognition is widely used in the retrieval, recognition, and management of various information to improve efficiency in various industries. Convolutional neural networks help people get rid of the feature of extracting information feature sets manually and significantly improve recognition efficiency. In this paper, a generalization-enhanced network recognition model is proposed using an improved lightweight convolutional neural network model. An improved method is used to adapt word recognition by the idea of pre-recognition segmentation. In addition, the generalization is enhanced by diversifying and pre-processing the dataset so that the algorithm can obtain noise-resistant performance and detail retention and allow the recognition system to recognize various types of scenes. The results show that the model achieves an accuracy of 93% on the test set. Compared with other classical network models, the model has higher recognition accuracy, faster convergence, and better generalization ability. The system elaborated in this paper can be used for devices with weak computer processing power.