  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.
