Non-Linear Segmentation of Touched Roman Characters Based on Genetic Algorithm

The segmentation accuracy of Roman cursive characters, especially touched characters, is essential for the high performance of Optical Character Recognition Systems. This paper presents a new approach for non-linear segmentation of multiple touched Roman cursive characters based on genetic algorithm. Initially, a possible segmentation zone is detected and then best segmentation path is evolved by genetic algorithm. The initial population is composed of each point column in possible segmentation zone. The individual coding, fitness function, crossover operator and mutation operator are also defined for this task. Experimental results on a test set extracted on the IAM benchmark database exhibit high segmentation accuracy up to 89.76%. Proposed approach can handle some complex types of touched cursive characters without special heuristic rules and recognition.

[1]  Yi Lu,et al.  Character segmentation in handwritten words - An overview , 1996, Pattern Recognit..

[2]  Horst Bunke,et al.  The IAM-database: an English sentence database for offline handwriting recognition , 2002, International Journal on Document Analysis and Recognition.

[3]  Ghazali Sulong,et al.  Simple and effective techniques for core-region detection and slant correction in offline script recognition , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[4]  Amjad Rehman,et al.  Self Organizing Features Map with Improved Segmentation to Identify Touching of Adjacent Characters in Handwritten Words , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[5]  Ventzislav Alexandrov.  Using critical points in contours for segmentation of touching characters , 2004, CompSysTech '04.

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

[7]  U. Jayarathna,et al.  New Segmentation Algorithm for Offline Handwritten Connected Character Segmentation , 2006, First International Conference on Industrial and Information Systems.

[8]  Kiril Boyanov.  Proceedings of the 5th international conference on Computer systems and technologies , 2004 .

[9]  G. Dimauro,et al.  Segmentation of numeric strings , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[10]  C. Tappert,et al.  A Genetic Algorithm for Constructing Compact Binary Decision Trees , 2009 .

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  Georges R. Harik,et al.  Foundations of Genetic Algorithms , 1997 .

[13]  Jhing-Fa Wang,et al.  Segmentation of Single- or Multiple-Touching Handwritten Numeral String Using Background and Foreground Analysis , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Changsong Liu,et al.  Multi-scale feature extraction and nested-subset classifier design for high accuracy handwritten character recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[16]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[17]  T. K. Bandopadhyaya,et al.  Journal of Theoretical and Applied Information Technology , 2022 .

[18]  Ioannis Pratikakis,et al.  Hybrid off-line OCR for isolated handwritten Greek characters , 2007 .

[19]  Yun Lei,et al.  A recognition based system for segmentation of touching handwritten numeral strings , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

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

[21]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[22]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[23]  Shuyan Zhao,et al.  Two-stage segmentation of unconstrained handwritten Chinese character , 2003, Pattern Recognit..