A Deep Learning-Based Text Detection and Recognition Approach for Natural Scenes

In this paper, we design a natural scene text detection and recognition model based on deep learning by model construction and in-depth study of wild scene text detection and recognition. This paper proposes a scene text recognition method based on connection time classification and attention mechanism for the situation where natural scene text is challenging to recognize due to the high complexity of text and background. The method converts the text recognition problem in natural scenes into a sequence recognition problem, avoiding the drawback of overall recognition performance degradation due to the difficulty of character segmentation. At the same time, the attention mechanism introduced can reduce the network complexity and improve the recognition accuracy. The performance of the improved PSE-based text detection algorithm in this paper is tested on the curved text datasets SCUT-ctw1500 and ICDAR2017 in natural scenes for comparison. The results show that the proposed algorithm achieves 88.5%, 77%, and 81.3% in the three indexes of accuracy, recall, and Fl value, respectively, without adding the pre-training module. The algorithm can detect text in any direction well without adding the pre-training module; the improved text recognition algorithm based on CRNN in this paper is tested on the natural scene dataset ICDAR2017, and the results show that the accuracy rate reaches 94.5% under the condition of no constraint, which is a good performance.