Optical font recognition in smartphone-captured images and its applicability for ID forgery detection

Chernyshova, Yulia S.; Aliev, M. Sh.; Gushchanskaia, Ekaterina; Sheshkus, Alexander

doi:10.1117/12.2522955

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…The light-weight ANN architectures that we propose in our framework are based on the previous papers of the authors. For example, in [54]- [56] we showed the capabilities of the light-weight architecture in OCR problems, in [58] we employed a light-weight neural network for both optical font recognition and OCR, and in [59] the ability of a light-weight ANN to detect vanishing points was demonstrated.…”

Section: Anns In ''On the Device'' Ocr Solutionsmentioning

confidence: 99%

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

Chernyshova¹,

Sheshkus²,

Arlazarov

2020

IEEE Access

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In this paper, we introduce an ''on the device'' text line recognition framework that is designed for mobile or embedded systems. We consider per-character segmentation as a language-independent problem and individual character recognition as a language-dependent one. Thus, the proposed solution is based on two separate artificial neural networks (ANN) and dynamic programming instead of employing image processing methods for the segmentation step or end-to-end ANN. To satisfy the tight constraints on memory size imposed by embedded systems and to avoid overfitting, we employ ANNs with a small number of trainable parameters. The primary purpose of our framework is the recognition of low-quality images of identity documents with complex backgrounds and a variety of languages and fonts. We demonstrate that our solution shows high recognition accuracy on natural datasets even being trained on purely synthetic data. We use MIDV-500 and Census 1961 Project datasets for text line recognition. The proposed method considerably surpasses the algorithmic method implemented in Tesseract 3.05, the LSTM method (Tesseract 4.00), and unpublished method used in the ABBYY FineReader 15 system. Also, our framework is faster than other compared solutions. We show the language-independence of our segmenter with the experiment with Cyrillic, Armenian, and Chinese text lines.

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Section: Anns In ''On the Device'' Ocr Solutionsmentioning

confidence: 99%

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

Chernyshova¹,

Sheshkus²,

Arlazarov

2020

IEEE Access

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“…(Lee and Kwak 2015) propose a method for extracting the passport MRZ using template matching, but only for images in which the passport is surrounded by a black border. (Chernyshova et al 2019) explored optical font recognition for forgery detection in passport MRZs. (Petrova and Bulatov 2019) discuss methods for correcting or post-processing passport MRZ recognition results.…”

Section: Passport Mrz Detection and Extractionmentioning

confidence: 99%

MRZ code extraction from visa and passport documents using convolutional neural networks

Liu,

James,

Gupta

et al. 2020

Preprint

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Detecting and extracting information from Machine-Readable Zone (MRZ) on passports and visas is becoming increasingly important for verifying document authenticity. However, computer vision methods for performing similar tasks, such as optical character recognition (OCR), fail to extract the MRZ given digital images of passports with reasonable accuracy. We present a specially designed model based on convolutional neural networks that is able to successfully extract MRZ information from digital images of passports of arbitrary orientation and size. Our model achieved 100% MRZ detection rate and 98.36% character recognition macro-f1 score on a passport and visa dataset.

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“…A detailed description of the protective elements can be found in the PRADO glossary [7]. For example, to protect a document, the following can be used: stamps [8,9], special fonts [10,11], holograms [12,13], hairs [14] and drawings fluorescing in ultraviolet light, various elements visible in the visible range and disappearing in the infrared, etc.…”

Section: Introductionmentioning

confidence: 99%

Building an optimal document authentication system

Bursikov,

Usilin,

Kunina

2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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In this work, a probabilistic approach to assessing the quality of a system for determining the authenticity of documents in the images is considered. The considered system is based on the aggregation of responses of various checks of the security elements of the document. We propose a probabilistic model of the document authentication system and the functional for evaluating the quality of the system. Based on them, we offer an approach for assessing the quality of the separate part and the whole system. Finally, the approach to constructing an optimal function of making a final decision is obtained.

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Optical font recognition in smartphone-captured images and its applicability for ID forgery detection

Cited by 11 publications

References 13 publications

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

MRZ code extraction from visa and passport documents using convolutional neural networks

Building an optimal document authentication system

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