Segmentation-free Connectionist Temporal Classification loss based OCR Model for Text Captcha Classification

• URL: http://arxiv.org/abs/2402.05417v1
• Date: Thu, 8 Feb 2024 05:18:11 GMT
• Title: Segmentation-free Connectionist Temporal Classification loss based OCR Model for Text Captcha Classification
• Authors: Vaibhav Khatavkar, Makarand Velankar and Sneha Petkar
• Abstract summary: We propose a segmentation-free OCR model for text captcha classification based on the connectionist temporal classification loss technique. The accuracy of the proposed model is compared with the state-of-the-art models and proves to be effective.
• Score: 7.37329190948762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Captcha are widely used to secure systems from automatic responses by distinguishing computer responses from human responses. Text, audio, video, picture picture-based Optical Character Recognition (OCR) are used for creating captcha. Text-based OCR captcha are the most often used captcha which faces issues namely, complex and distorted contents. There are attempts to build captcha detection and classification-based systems using machine learning and neural networks, which need to be tuned for accuracy. The existing systems face challenges in the recognition of distorted characters, handling variable-length captcha and finding sequential dependencies in captcha. In this work, we propose a segmentation-free OCR model for text captcha classification based on the connectionist temporal classification loss technique. The proposed model is trained and tested on a publicly available captcha dataset. The proposed model gives 99.80\% character level accuracy, while 95\% word level accuracy. The accuracy of the proposed model is compared with the state-of-the-art models and proves to be effective. The variable length complex captcha can be thus processed with the segmentation-free connectionist temporal classification loss technique with dependencies which will be massively used in securing the software systems.

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