Segmentation-free Connectionist Temporal Classification loss based OCR
  Model for Text Captcha Classification
Vaibhav Khatavkar Makarand Velankar Sneha Petkar
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.