Recognition of broken and noisy handwritten characters using statistical methods based on a broken-character-mending algorithm

A broken-character-mending algorithm is proposed to deal with broken characters. The algorithm can mend various kinds of broken characters if the number of missing pixels is smaller than the number of existing pixels in the mending masks. It also has no negative effects on the pixels that do not need mending. Mending masks of different sizes can be combined and the same mask can be applied several times to improve the mending result. The algorithm has been tested on some seriously broken characters, in which up to 50% of pixels are deleted randomly. The recognition process is based on statistical analysis com- bined with a shape recognition method. In our experiment, the recogni- tion rate of the statistical method increased by about 7% after using the character-mending algorithm. The substitution rate decreased from 7.4 to 2.3% and the reliability increased from 91.9 to 97.6% after using the broken-character-mending algorithm. © 1997 Society of Photo-Optical Instru- mentation Engineers. (S0091-3286(97)02305-2)

[1]  Majid Ahmadi,et al.  Recognition of handwritten numerals with multiple feature and multistage classifier , 1995, Pattern Recognit..

[2]  Youngtae Park.  A comparison of neural net classifiers and linear tree classifiers: Their similarities and differences , 1994, Pattern Recognit..

[3]  Wen-Tsuen Chen,et al.  A hierarchical deformation model for online cursive script recognition , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[4]  P. Nagabhushan,et al.  A new approach to handwritten character recognition , 1996 .

[5]  Burzin Bhavnagri.  A method for representing shape based on an equivalence relation on polygons , 1994, Pattern Recognit..

[6]  Atul K. Chhabra,et al.  High-order statistically derived combinations of geometric features for handprinted character recognition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[7]  Ching Y. Suen,et al.  A sequential method of extracting contour chains from an image , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[8]  Jack Sklansky,et al.  Automated design of linear tree classifiers , 1990, Pattern Recognit..

[9]  P.-F. YANG,et al.  Min-max classifiers: Learnability, design and application , 1995, Pattern Recognit..

[10]  Hyun Seung Yang,et al.  A neural network capable of learning and inference for visual pattern recognition , 1994, Pattern Recognit..

[11]  Basil G. Mertzios,et al.  Shape recognition with a neural classifier based on a fast polygon approximation technique , 1994, Pattern Recognit..

[12]  Wen-Tsuen Chen,et al.  A hierarchical deformation model for on-line cursive script recognition , 1994, Pattern Recognit..

[13]  Ching Y. Suen,et al.  Computer recognition of unconstrained handwritten numerals , 1992, Proc. IEEE.