Localization of characters horizontal bounds in text line images with fully convolutional network
Yulia S. Chernyshova, Anastasiya Chirvonaya, Alexander V. Sheshkus · 2020
Character segmentation is one of the crucial problems of modern text line recognition methods. In this paper, we propose a per-character segmentation method based on the light weight convolutional neural network (CNN) which is suitable for on-premise applications for various mobile devices. The distinctive feature of our method is that it provides the coordinates of the start and end points of each character, not the coordinates of the “cut” between two characters. It allows us to utilize known geometrical properties of glyphs efficiently. Consequently, the target character images are not flawed because of characters intersections or wide spaces. We present the results measured for text lines with various letter spacing. Results illustrate that the proposed method decreases the segmentation error rate for the majority of test datasets.