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.
Highlights
The object recognition problem, which has been extensively studied in past decades, has a wide range of real applications
The experimental results obtained using the proposed algorithmfor the quality assessment of projectively distorted field images are presented and compared with the performance of the algorithm described in [25]
In this paper, we consider the problem of quality assessment of a field image restored from a projectively distorted source document image
Summary
The object recognition problem, which has been extensively studied in past decades, has a wide range of real applications. The error cost largely depends on the problem’s specifics or on the particular application of the developed system In many areas, such as identity verification [1,2,3], self-driving vehicles [4,5], and industrial diagnostics [6,7], incorrect recognition can cause financial loss or even harmful health outcomes. There are three main approaches to reliability evaluation: recognition confidence analysis, pixel-based image quality assessment, and geometric image quality assessment. These approaches work at different recognition stages and, can be applied together
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