In this paper, we propose a new method to detect monospaced font in text line images. Although many authors address more complex problems of text recognition or font recognition, this problem is still challenging when dealing with camera-captured images of identity documents. However, such a font characteristic can be useful in document authentication. These images usually contain complex backgrounds and various distortions. Our approach is based on a segmentation neural network and Fourier Transform for detecting “strong” periodic components in the segmentor output. The experimental results show that the combination of neural network and Fourier Transform deals with the task of monospaced font detection more effectively than the same Fourier analysis applied to the results of an image processing method for segmentation. The main advantage of the neural network is that its output does not depend on background, font and characters characteristics directly.
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.
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