A Comprehensive Study on Character Segmentation

In identifying the characters from a given image, character segmentation plays an important role. In a given line of text, first, we have to segment the words. Then, in each word there will be a character-by-character segmentation. There have been some rapid developments in this area. Many algorithms have been implemented to increase the accuracy range and decrease the word error rate. This paper aims to provide a review of some of the developments that have happened in this domain.

[1]  Mayur M Patil,et al.  Handwritten Kannada Document Image Processing using Optical Character Recognition , 2016 .

[2]  Ahmed Bouridane,et al.  HACDB: Handwritten Arabic characters database for automatic character recognition , 2013, European Workshop on Visual Information Processing (EUVIP).

[3]  Hermann Ney,et al.  Moment-Based Image Normalization for Handwritten Text Recognition , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[4]  Youbao Tang,et al.  Offline Text-Independent Writer Identification Based on Scale Invariant Feature Transform , 2014, IEEE Transactions on Information Forensics and Security.

[5]  Oendrila Samanta,et al.  Script independent online handwriting recognition , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[6]  Sunanda Dixit,et al.  Kannada text line extraction based on energy minimization and skew correction , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[7]  Ching Y. Suen,et al.  Statistical Hypothesis Testing for Handwritten Word Segmentation Algorithms , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[8]  Horst Bunke,et al.  A full English sentence database for off-line handwriting recognition , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[9]  Jun Tan,et al.  A new handwritten character segmentation method based on nonlinear clustering , 2012, Neurocomputing.

[10]  Ernest Valveny,et al.  Query by string word spotting based on character bi-gram indexing , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[11]  K. C. Santosh,et al.  Handwritten and machine printed text separation from Kannada document images , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[12]  C. Naveena,et al.  Handwritten character segmentation for Kannada scripts , 2012, 2012 World Congress on Information and Communication Technologies.

[13]  Venu Govindaraju,et al.  Online Handwritten Cursive Word Recognition Using Segmentation-Free MRF in Combination with P2DBMN-MQDF , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[14]  R. Krishnan,et al.  Broken kannada character recognition — A neural network based approach , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[15]  Ghazali Sulong,et al.  Cursive script segmentation with neural confidence , 2011 .

[16]  E ManjunathA,et al.  Implementing Kannada Optical Character Recognition on the Android Operating System for Kannada Sign Boards , 2013 .

[17]  Sunanda Dixit SOUTH INDIAN TAMIL LANGUAGE HANDWRITTEN DOCUMENT TEXT LINE SEGMENTATION TECHNIQUE WITH AID OF SLIDING WINDOW AND SKEWING OPERATIONS , 2013 .

[18]  Liang Huang,et al.  Keyword spotting in unconstrained handwritten Chinese documents using contextual word model , 2013, Image Vis. Comput..

[19]  A. P. Jagadeesh Chandra,et al.  Line and word segmentation of Kannada handwritten text documents using projection profile technique , 2016, 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT).

[20]  M. Thungamani,et al.  KANNADA TEXT EXTRACTION FROM IMAGES AND VIDEOS FOR VISION IMPAIRED PERSONS , 2011 .