A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

Julia Shemiakina, Elena Limonova, Natalya Skoryukina, Vladimir V. Arlazarov and Dmitry P. Nikolaev
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Julia Shemiakina: Smart Engines Service LLC, 117312 Moscow, Russia
Elena Limonova: Smart Engines Service LLC, 117312 Moscow, Russia
Natalya Skoryukina: Smart Engines Service LLC, 117312 Moscow, Russia
Vladimir V. Arlazarov: Smart Engines Service LLC, 117312 Moscow, Russia
Dmitry P. Nikolaev: Smart Engines Service LLC, 117312 Moscow, Russia

Mathematics, 2021, vol. 9, issue 17, 1-22

Abstract: In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.

Keywords: projective distortion; image quality assessment; document analysis; text recognition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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