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.
Abstract (translated)
Captcha 广泛用于通过区分计算机响应与人类响应来保护系统免受自动回复。文本、音频、视频和基于图像的图像识别（OCR）用于创建 captcha。基于文本的 OCR captcha 是最常见的 captcha，它面临复杂和扭曲的内容的问题。尝试使用机器学习和神经网络基于 captcha 检测和分类构建系统，这些系统需要进行精度调整。现有的系统在识别扭曲的 characters、处理变长 captcha 和寻找序列依赖方面面临挑战。在本文中，我们提出了一种基于连接主义时间分类损失技术文本captcha分类的分割免费 OCR 模型。所提出的模型在公开可用的 captcha 数据集上进行训练和测试。所提出的模型在字符级别具有 99.80\% 的准确率，而在单词级别具有 95\% 的准确率。与最先进的模型进行比较证明效果显著。因此，可以使用基于分割免费连接主义时间分类损失技术处理变长复杂 captcha。
URL
https://arxiv.org/abs/2402.05417
