A Cross Correlation Approach for Breaking of Text CAPTCHA

Online web service providers generally protect themselves through CAPTCHA. A CAPTCHA is a type of challenge-response test used in computing as an attempt to ensure that the response is generated by a person. CAPTCHAS are mainly instigated as distorted text which the handler must correctly transcribe. Numerous schemes have been proposed till date in order to prevent attacks by Bots. This paper also presents a cross correlation based approach in breaking of famous service provider's text CAPTCHA i.e. PayPal.com and the other one is of India's most visited website IRCTC.co.in. The procedure can be fragmented down into 3 firmly tied tasks: pre-processing, segmentation, and classification. The pre-processing of the image is performed to remove all the background noise of the image. The noise in the CAPTCHA are unwanted on pixels in the background. The segmentation is performed by scanning the image for on pixels. The organization is performed by using the association values of the inputs and templates. Two types of templates have been used for classification purpose. One is the standard templates which give 30% success rate and other is the noisy templates made from the captcha images and success rate achieved with these is 100%.

[1]  Patrick S. P. Wang,et al.  Character segmentation techniques for handwritten text-a survey , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[2]  Kurt Alfred Kluever 4005-898-01 Independent Study Report Character Segmentation and Classification , 2008 .

[3]  Anil K. Jain,et al.  Feature extraction methods for character recognition-A survey , 1996, Pattern Recognit..

[4]  Shijian Lu,et al.  Accurate recognition of words in scenes without character segmentation using recurrent neural network , 2017, Pattern Recognit..

[5]  Salman Amin Khan Character segmentation heuristics for check amount verification , 1998 .

[6]  Yi Lu,et al.  Machine printed character segmentation --; An overview , 1995, Pattern Recognit..

[7]  Abhishek Bal,et al.  An Improved Method for Text Segmentation and Skew Normalization of Handwriting Image , 2018 .

[8]  Ya Su,et al.  A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  T. Sree Sharmila,et al.  Effective Printed Tamil Text Segmentation and Recognition Using Bayesian Classifier , 2017 .

[10]  Anil K. Jain,et al.  Text segmentation using gabor filters for automatic document processing , 1992, Machine Vision and Applications.

[11]  Phoey Lee Teh,et al.  Text Segmentation Techniques: A Critical Review , 2018 .

[12]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[13]  Hiromitsu Yamada,et al.  Optical Character Recognition , 1999 .

[14]  Eric Lecolinet,et al.  A Survey of Methods and Strategies in Character Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Yeuan-Kuen Lee,et al.  An efficient segmentation algorithm for CAPTCHAs with line cluttering and character warping , 2010, Multimedia Tools and Applications.

[16]  Roy L. Hoffman,et al.  Segmentation Methods for Recognition of Machine-Printed Characters , 1971, IBM J. Res. Dev..

[17]  Anil K. Jain,et al.  A Generic System for Form Dropout , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Hassan Basri,et al.  Real time road sign recognition system using artificial neural networks for bengali textual information box , 2008, 2008 International Symposium on Information Technology.