High Accuracy Text Detection using ResNet as Feature Extractor
Chi-Shin Yang, Chen-Chiung Hsieh · 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · 2019
Traditional OCR system cannot handle scene texts. Text detection in natural scene images is much more difficult than the recognition of text in scanned document images because of its complicating background. Popular existing methods for characters segmentation are CTPN, EAST and PixelLink. However, they are not very capable of dealing with the small and densely character in large image, and connected characters. To cope with these problems, we adopted above popular character segmentation networks CTPN or EAST as the main structure and proposed using ResNet as the feature extraction network due to its excellent sensitivity of tiny features among those existing methods. The experimental result shows that the feature extraction network can affect the precision of locating text significantly. In the experiment with ICDAR dataset, the effect of deeper depth and larger width of ResNet on EAST is notable.