Monospaced font detection using character segmentation and Fourier transform

Anastasiya N. Chirvonaya; Alexander V. Sheshkus; Vladimir L. Arlazarov

doi:10.1117/12.2559373

31 January 2020 Monospaced font detection using character segmentation and Fourier transform

Anastasiya N. Chirvonaya, Alexander V. Sheshkus, Vladimir L. Arlazarov

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143317 (2020) https://doi.org/10.1117/12.2559373
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

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.

Citation Download Citation

Anastasiya N. Chirvonaya, Alexander V. Sheshkus, and Vladimir L. Arlazarov "Monospaced font detection using character segmentation and Fourier transform", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143317 (31 January 2020); https://doi.org/10.1117/12.2559373

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