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

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 metho...

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Bibliographic Details
Published in:Mathematics (Basel) 2021-09, Vol.9 (17), p.2155
Main Authors: Shemiakina, Julia, Limonova, Elena, Skoryukina, Natalya, Arlazarov, Vladimir V., Nikolaev, Dmitry P.
Format: Article
Language:English
Subjects:
Accuracy
Cameras
Datasets
Deep learning
Distortion
document analysis
Electronic devices
Embedded systems
Identification documents
Image quality
image quality assessment
Image restoration
Localization
Mathematics
Methods
Neural networks
Object recognition
projective distortion
Quality assessment
text recognition
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9172155