关键词: 文本识别, 深度学习, 循环卷积网络, Transformer模型

Abstract: In the scenario of live-streaming e-commerce, through the analysis of the bullet screen information sent by consumers, whether the actual evaluation of current commodities is the same as that described by the anchor can be reflected to a certain extent, which plays a regulatory role in the promotion of counterfeit products in the live-streaming industry. For the special characteristics of bullet screen text recognition, in this thesis, a real-time bullet screen recognition network based on improved CRNN (convolutional recurrent neural network) is proposed to solve the problems of incomplete extraction of text feature information by CRNN algorithm in complex background environment. Therefore, the designed network adopts an encoding and decoding structure to enhance the feature extraction module to solve the problem of feature loss during feature extraction caused by the small pixel area of the bullet screen. Moreover, a Transformer model is used to construct long-distance global feature relationships for input frames to strengthen the ability of network model to capture and extract bullet screen information. And the extracted feature information is sequentially modeled and transcribed to obtain the specific bullet screen semantic information. The experimental results show that the detection accuracy of the designed network is tested experimentally up to 0.926 on the test set, which improves the accuracy value by 0.101 on average.

Key words: bullet screen text, deep learning, recurrent convolutional network, Transformer model