Affiliations: [a]
Ordos Institute of Technology, Inner Mongolia, Ordos,China
| [b]
North China Electric Power University, Beijing, China
| [c]
North China University of Technology, Beijing, China
| [d]
Hunan Institute of Engineering, Hunan, Xiangtan, China
Correspondence:
[*]
Corresponding author. Yuejie Li, Ordos Institute of Technology, Inner Mongolia, Ordos, 017000, China. E-mail: [email protected].
Abstract: Text detection and recognition are widely used in daily life. Although it is a very rich market, it has very difficult challenges in practical application. The complex and changeable natural scenes lead to the complex background of the text in the image, which also reflects the research value of text information recognition and extraction in natural scenes. To solve the many problems faced by the recognition of Chinese characters, such as complex shapes and diverse structures, this paper uses VGG16 to extract features and introduces a two-layer bidirectional LSTM network. It improves Faster R-CNN by using a RPN to extract candidate boxes and adjust the position of candidate regions. In this paper, the improved model Faster BLSTM-CNN is tested, and the effectiveness experiment of feature extraction, the difference comparison before and after the improvement of the algorithm, and the comparison experiment with the traditional recognition algorithm are carried out respectively. And it finally carried out an experimental comparison of the combination of text recognition and positioning, and obtained the results. The algorithm Faster BLSTM-CNN in this paper is better in the localization and recognition of Chinese characters in the dataset. In the natural scene, the recognition rate of Faster BLSTM-CNN in this paper is 81.54%, the positioning accuracy is 88.14%, and the detection speed is 86 ms, which has improved performance. Therefore, the improvement of Faster R-CNN is effective. It can effectively locate and recognize Chinese characters in natural scenes.
Keywords: Text recognition, deep learning, algorithm optimization, Faster R-CNN
