Development of a character CAPTCHA recognition system for the visually impaired community using deep learning

Zhang, Xiaohui; Liu, Xinhua; Sarkodie-Gyan, Thompson; Li, Zhixiong

doi:10.1007/s00138-020-01160-8

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…A new CAPTCHA using Chinese characters was also created, and it removed the imbalance issue of class for model training. A statistical evaluation led to a higher success rate ( Zhang et al, 2021 ). A data selection approach automatically selected data for training purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

Lu¹,

Huang²,

Meraj

et al. 2022

PeerJ Computer Science

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A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA’s need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA’s. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.

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Section: Literature Reviewmentioning

confidence: 99%

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

Lu¹,

Huang²,

Meraj

et al. 2022

PeerJ Computer Science

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show abstract

“…El más conocido en internet es el sistema Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart) que se sirve de diversos elementos visuales en sus tests para reconocer a un robot (Captcha visual) (Brodić & Amelio, 2020). En la actualidad, el sistema reCAPTCHA de Google analiza además las cookies activas, la dirección IP y el comportamiento del usuario con el movimiento del ratón en el ordenador (Zhang, Liu, Sarkodie-Gyan et al, 2021). Los robots aprendieron a sortear este sistema de protección, pero su comportamiento es más detectable por la lógica automática del movimiento, y en ello se apoya la seguridad de Google (Li et al, 2019).…”

Section: La Identidad Visual Algorítmicaunclassified

La visualidad algorítmica: una aproximación social a la visión artificial en la era post internet

Medina

Marín

2022

Arte, individuo y sociedad (Internet)

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En el marco de los estudios visuales, se observa un desarrollo de singulares prácticas cuya orientación tecnológica está basada en la innovación de algoritmos de inteligencia artificial. En este contexto, la investigación busca revelar la emergencia de una nueva interpretación de la visualidad, concretamente, mediante el análisis de dos líneas principales (cuya relación se trata de mostrar): por una parte, la visión artificial y su extensión en el universo posinternet de las redes sociales y de la web, donde la imagen pierde su significado simbólico y su dimensión estética para valorarse como una información que cambia el estado de un sistema; y, por otro lado, el conocimiento social del mundo virtual a través del uso, la actitud y el comportamiento humano con los algoritmos inteligentes. Mediante la revisión bibliográfica multidisciplinar, como método principal, las conclusiones apuntan a una importante presencia de una visualidad dependiente de las máquinas inteligentes, que aportan un mayor enriquecimiento del estudio tanto de la naturaleza humana como de la realidad social en el entorno virtual.

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“…Text extraction from natural images has been the center of attention for the research community in computer vision. It has the potential to be used in a variety of real-world applications, such as assisting visually impaired persons [1], autonomous traffic sign recognition [2], scene understanding [3], robot navigation [4] and license plate detection [5]. Researchers have well-studied text extraction in documents, and many commercial products are available, having recognition accuracy of more than 99% [6] on the documented text.…”

Section: Introductionmentioning

confidence: 99%

Towards AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension

et al. 2024

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Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a largescale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.

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Development of a character CAPTCHA recognition system for the visually impaired community using deep learning

Cited by 9 publications

References 45 publications

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

La visualidad algorítmica: una aproximación social a la visión artificial en la era post internet

Towards AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension

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