Assessing the Relationship Between Binarization and OCR in the Context of Deep Learning-Based ID Document Analysis

Sánchez-Rivero, Rubén; Bezmaternykh, Pavel; Morales-González, Annette; Mata, Francisco José Silva; Bulatov, Konstantin B.

doi:10.1007/978-3-030-89691-1_14

Cited by 2 publications

(2 citation statements)

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“…In this experiment, the image data is a subset of T 500 , with ten documents written in non-Latin alphabets excluded. The best character size for each DIB algorithm, as determined in [7], is used, and all the template images are accordingly resized before being processed by the binarization algorithms. The set of filtered and preprocessed templates is 500 T     , with   =   {B id , B gt } and   = .…”

Section: Fig 2 Description Of the First Experimentsmentioning

confidence: 99%

“…This work is an extension of the study [7] with additional experimental details and insights. The contributions can be summarized as follows: (a) an experimental analysis of OCR modules accuracy over binarization outcome on the MIDV-500 and MIDV-2020 subsets; (b) an experimental analysis of input image quality influence over the performance of the reviewed modules on the MIDV-500; (c) an analysis of a U-Net based solution accuracy retrained with domain specific data from the MIDV-500; (d) a manual pixel-wise annotation of ID document templates from the MIDV-500; (e) a manual image quality annotation for the MIDV-500.…”

Section: Introductionmentioning

confidence: 95%

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A joint study of deep learning-based methods for identity document image binarization and its influence on attribute recognition

Sánchez-Rivero,

Bezmaternykh,

Gayer

et al. 2023

Computer Optics

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Text recognition has benefited considerably from deep learning research, as well as the preprocessing methods included in its workflow. Identity documents are critical in the field of document analysis and should be thoroughly researched in relation to this workflow. We propose to examine the link between deep learning-based binarization and recognition algorithms for this sort of documents on the MIDV-500 and MIDV-2020 datasets. We provide a series of experiments to illustrate the relation between the quality of the collected images with respect to the binarization results, as well as the influence of its output on final recognition performance. We show that deep learning-based binarization solutions are affected by the capture quality, which implies that they still need significant improvements. We also show that proper binarization results can improve the performance for many recognition methods. Our retrained U-Net-bin outperformed all other binarization methods, and the best result in recognition was obtained by Paddle Paddle OCR v2.

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Section: Fig 2 Description Of the First Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%