Efficient Scene Text Recognition Model Built with PaddlePaddle Framework

Scene text recognition is a frontier research direction in optical character recognition (OCR) technology. Considering that the compatibility of the existing CRNN method under the PaddlePaddle framework is not friendly, and the recognition accuracy needs to be improved. Therefore, this paper proposes an efficient RBC model constructed by fusing ResNet34 and Bi-LSTM backbone network and Connectionist Temporal Classification (CTC) transcription method to solve the problem of scene text recognition. By coding based on the PaddlePaddle framework and experimenting with shared dataset, the results show that the RBC model is well compatible with the PaddlePaddle framework, and the average accuracy has reached 85.91%, which is 1.8% higher than the optimal baseline model. The data and code of this article, please link to the URL https://aistudio.baidu.com/aistudio/projectdetail/1911502?=1622688767906.