关键词: 文字检测, 文字定位, 文字识别, 卷积神经网络, 多方向文字, 谱聚类

Abstract:

In order to improve the accuracy of the classic target detection algorithms for text localization in natural scenes, and to overcome the problem of incorrect segmentation of Chinese characters by traditional character detection models due to the non-connectivity between strokes, a direct and efficient Chinese text spotting method is proposed in this paper. Text box is detected by EAST algorithm. The detected text box is adjusted to make it more compact and contain text more comprehensively, which comprises the connected component extraction, Chinese character segmentation and text shape approximation. The extracted text regions are corrected and transcribed. Experimental results show that while maintaining 3.2 frame per second, the proposed algorithm has F-score of 83.5%, 72.8% and 81.1% in text positioning task of three multi-oriented text datasets, ICDAR2015, ICDAR2017-MLT and MSRA-TD500, respectively. The ablation experiment verifies the effectiveness of each module in the proposed algorithm. The performance of the comprehensive evaluation task of detection and recognition on the ICDAR2015 data set also proves that the proposed method has achieved better performance than some of the latest methods.

Key words: text detection, text spotting, text recognition, convolution neural network, multi-oriented text, spectral clustering