Segmentation Methods for Hand Written Character Recognition

Hand written Character Recognition is area of research since many years. Automation of existing manual system is need of most industries as well as government areas. Recognition of hand written characters is a demand for many fields. In this paper we have discussed our approach for hand written character segmentation. This paper discusses various methodologies to segment a text based image at various levels of segmentation. This paper serves as a guide for people working on the text based image segmentation area of Computer Vision. First, the need for segmentation is justified in the context of text based information retrieval. Then, the various factors affecting the segmentation process are discussed. Followed by the levels of text segmentation are explored. Also, the available techniques with their advantages and weaknesses are reviewed, along with directions for quick referral are suggested. At last, we have given our approach to text segmentation in brief.

[1]  R. Manmatha,et al.  A scale space approach for automatically segmenting words from historical handwritten documents , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Shravani Krishna Rau,et al.  Off-line Handwritten Kannada Text Recognition using Support Vector Machine using Zernike Moments , 2011 .

[3]  M Swamy Das,et al.  SEGMENTATION OF OVERLAPPING TEXT LINES , CHARACTERS IN PRINTED TELUGU TEXT DOCUMENT IMAGES , 2010 .

[4]  P. S. Sastry,et al.  A font and size-independent OCR system for printed Kannada documents using support vector machines , 2002 .

[5]  Vijaya Kumar Koppula,et al.  Comparative Study of Text Line Segmentation Algorithms on Low Quality Documents , 2012 .

[6]  Luiz Eduardo Soares de Oliveira,et al.  Evaluation of different feature sets in an OCR free method for word spotting in printed documents , 2010, SAC '10.

[7]  Darko Brodic,et al.  A New Approach to Water Flow Algorithm for Text Line Segmentation , 2011, J. Univers. Comput. Sci..

[8]  G. Rama Mohan Babu,et al.  An Efficient Feature Extraction and Classification of Handwritten Digits Using Neural Networks , 2011 .

[9]  Mamta Maloo,et al.  Gujarati Script Recognition: A Review , 2011 .

[10]  Andrew D. Bagdanov,et al.  Projection profile based skew estimation algorithm for JBIG compressed images , 1998, International Journal on Document Analysis and Recognition.

[11]  Ioannis Pratikakis,et al.  Text line and word segmentation of handwritten documents , 2009, Pattern Recognit..

[12]  Rosli Salleh,et al.  A Real-time Line Segmentation Algorithm for an Offline Overlapped Handwritten Jawi Character Recognition Chip , 2007 .

[13]  Azriel Rosenfeld,et al.  A method of detecting the orientation of aligned components , 1986, Pattern Recognit. Lett..

[14]  Chang-Ping Liu,et al.  A two-stage handwritten character segmentation approach in mail address recognition , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[15]  Sargur N. Srihari,et al.  Interpretation of handwritten addresses in US mailstream , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[16]  Manish Kumar Sahu,et al.  Handwritten Character Recognition using Neural Network , 2017 .

[17]  Saurin Sheth,et al.  Text-based Image Segmentation Methodology , 2014 .

[18]  C. Halatsis,et al.  Line And Word Segmentation of Handwritten Documents , 2008 .

[19]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[20]  Guo-hong Li,et al.  An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration , 2004, Journal of Zhejiang University. Science.

[21]  Brijesh Verma,et al.  Pattern Recognition Technologies and Applications: Recent Advances , 2008 .

[22]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[23]  George D. C. Cavalcanti,et al.  Text Line Segmentation Based on Morphology and Histogram Projection , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[24]  Ming Chen,et al.  A robust skew detection algorithm for grayscale document image , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[25]  Fatos T. Yarman-Vural,et al.  Repulsive attractive network for baseline extraction on document images , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[26]  Anil K. Jain,et al.  Text information extraction in images and video: a survey , 2004, Pattern Recognit..

[27]  R. D. Sudhaker Samuel,et al.  A simple and efficient optical character recognition system for basic symbols in printed Kannada text , 2007 .

[28]  Paolo Nesi,et al.  Projection based segmentation of musical sheets , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[29]  Xiaoyan Zhu,et al.  A new algorithm for handwritten character recognition , 2001, ICIP.

[30]  Nafiz Arica,et al.  An overview of character recognition focused on off-line handwriting , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[31]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.