Author : Shweta Shevgekar 1
Date of Publication :7th January 2017
Abstract: Handwritten character recognition is a demanding task in the image processing because handwriting varies from person to person. And also handwriting styles, sizes and its orientation make it complex. Applications like, handwritten text in reading bank cheques, Zip Code recognition and for removing the problem of handling documents manually, digital data is necessary. Recognition of handwritten characters using either a scanned document, or direct acquisition of image using Mat lab, followed by the implementation of various other Mat lab toolboxes like Image Processing to process the scanned or acquired image. Here OCR block diagram explained that how character are recognize accurately. Many feature-based algorithms are well-suited for character recognition like like SIFT, Language Independent Text-Line Extraction, Thresholding, Robust, Training, Ullman Algorithm, Structured Learning, ORB(oriented fast & rotated brief), SURF. But Oriented FAST and Rotated BRIEF (ORB) is a very fast binary descriptor which is faster than Scale-invariant feature transform (SIFT), it can be verified through experiments.Fast key point detector and BRIEF descriptor are important because of they have best performance and resonable cost. The recognize method for object recognition is Scale invariant feature transform (SIFT), which is very useful for feature extraction but it is computationally difficult due to its weighty workload required in local feature extraction and matching operation. Therefore for better performance and low complexity,ORB provides better solution.
 R. Plamondon and G. Lorette, â€•Automatic signature verification andwriter identification—The stateof the art,â€– Pattern Recognit., vol. 22no. 2, pp. 107–131, Feb. 1989.
D. Lowe, â€•Distinctive image features from scaleinvariant keypoints,â€–Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.
 M. Bulacu and L. Schomaker, â€•Text-independent writeridentification and verification using textural and allographicfeatures,â€– IEEE Trans.Pattern Anal. Mach. Intell., vol. 29, no. 4, pp.701–717, Apr. 2007.
Chan, S. K., Viard-Gaudin, C., Tay, Y. H. (2008)"Online writerIdentification using character prototypes distributions,"InProceedings of SPIE - The International Society for OpticalEngineering.
 L. Du, X. You, H. Xu, Z. Gao, and Y. Tang, â€•Wavelet domain localbinary pattern features for writer identification,â€– in Proc. 20th Int. Conf.Pattern Recognit., Istanbul, Turkey, 2010, pp. 3691–3694.
 I. Siddiqi and N. Vincent, â€•Text independent writer recognition usingredundant writing patterns with contourbased orientation and curvaturefeatures,â€– Pattern Recognit., vol. 43, no. 11, pp. 3853–3865, Nov. 2010.
 R. Jain and D. Doermann, â€•Offline writer identification using k-adjacentsegments,â€– in Proc. Int. Conf. Document Anal. Recognit., Beijing, China,2011, pp. 769–773.
 R. Hanusiak, L. Oliveira, E. Justino, and R. Sabourin, â€•Writer verificationusing texture-based features,â€– Int. J. Document Anal. Recognit.vol. 15, no. 3, pp. 213–226, Sep. 2012.
 D. Bertolini, L. Oliveira, E. Justino, and R. Sabourin, â€•Texture-baseddescriptors for writer identification and verification,â€– Expert Syst. Appl.,vol. 40, no. 6, pp. 2069– 2080, May 2013.
 G. Ghiasi and R. Safabakhsh, â€•Offline textindependent writer identificationusing codebook and efficient code extraction methods,â€– ImageVis. Comput., vol. 31, no. 5, pp. 379–391, May 2013.
 Xiangqian Wu, Member, IEEE, Youbao Tang, and WeiBu, Member, IEEE,â€•Offline Text-Independent Writer Identification Based on Scale Invariant Feature Transformâ€–,IEEETRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, MARCH 2014
 Prashant Aglave1, Vijaykumar.S. Kolkure2 ,Implementation of high performance feature extraction method using oriented fast and rotated brief algorithm,IJRET: International Journal of Research in Engineering and Technology.
 Jewoong Ryu, Hyung Il Koo, Member, IEEE,and NamIk Cho, Senior Member, IEEE,â€•Word Segmentation Method for Handwritten Documents based on Structured Learningâ€–,IEEE SIGNAL PROCESSING LETTERS, VOL. 22, NO. 8, AUGUST 2015.