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The process of captcha breaking can be categorized into two different methods. It can be divided into two different ways: CAPTCHAs are broken by either classifying the CAPTCHA into a predefined set of characters or by using a CAPTCHA solver (Kumar, 201
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A study into the usage of GAN was tested on a Weibo dataset and showed results of 59.1% accuracy (Wang et al., 2020). The study of CAPTCHAs is a research topic that is being actively investigated, and there is no conclusion on how to perform CAPTCHA recognition. More work is required on the development of new CAPTCHA schemes and robust CAPTCHA generation techniques.
CAPTCHAs can be easily solved if they are generated too easily. This is the reason why there are numerous CAPTCHAs which are hard to solve by humans. The classification of CAPTCHAs is performed by machine learning and NLP, and classifiers are generally categorized into three broad categories: decision trees, neural networks, and support vector machines (Kim et al., 2006). An automatic CAPTCHA solution that was used for text generation was based on a hybrid deep learning model with an attention mechanism (Kim & An, 2015). It was suggested that the performance of this architecture was more optimized than traditional neural networks. Support vector machines and deep learning models were shown to achieve 76.5% accuracy on a Weibo dataset and 59.4% accuracy on a dataset in Korea (Kim et al., 2006).
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