Recognizer characterisation for combining handwriting recognition results at word level

The paper concentrates on the combination of results of multiple recognizers at the word level. Two approaches are presented: word list merging and linear combination. Word list merging requires no knowledge about the individual recognizers. The linear combination is an attempt to exploit the information about characteristics of individual recognizers. This appears more complex than in the case of combination of results at the character level. Recognition of words is influenced by more factors, which can independently affect the recognition process. Characterisation of recognizers, used for word level combination, is more complex and requires more than a simple consideration of recognition success and failure. The concept of handwriting data characterisation is defined. A number of handwriting characteristics are extracted and used to guide the combination process. The choice of characteristics is made in the context of recognition methods used. No attempt at general characterisation of handwriting is made. The relationship between handwriting characteristics and recognition results is observed and used to obtain characteristics of individual recognizers. Results of the two combination methods are reported and compared with another frequently used method for results combination, the Borda count.

[1]  Lindsay J. Evett,et al.  Multiple word segmentation with interactive look-up for cursive script recognition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[2]  Eric Lecolinet,et al.  A multi-classifier combination strategy for the recognition of handwritten cursive words , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[3]  Ching Y. Suen,et al.  Combination of multiple classifiers with measurement values , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[4]  Nasser Sherkat,et al.  THE USE OF WORD SHAPE INFORMATION FOR CURSIVE SCRIPT RECOGNITION , 1994 .

[5]  Xianoning Ling,et al.  Combining opinions form several experts , 1989, Appl. Artif. Intell..

[6]  J. Franke,et al.  A comparison of two approaches for combining the votes of cooperating classifiers , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[7]  Sargur N. Srihari,et al.  On multiple classifier systems for pattern recognition , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[8]  Ching Y. Suen,et al.  Complementary algorithms for the recognition of totally unconstrained handwritten numerals , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[9]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[10]  C.Y. Suen,et al.  Associative switch for combining multiple classifiers , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[11]  I. Yamashita,et al.  State of the art of handwritten numeral recognition in Japan-The results of the first IPTP character recognition competition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[12]  Donald P. D'Amato.  Machine vision applications in character recognition and industrial inspection : 10-12 February 1992, San Jose, California , 1992 .

[13]  Sargur N. Srihari,et al.  Regression approach to combination of decisions by multiple character recognition algorithms , 1992, Electronic Imaging.

[14]  Sargur N. Srihari,et al.  Word Recognition With Multi-Level Contextual Knowledge , 1991 .

[15]  Lindsay J. Evett,et al.  Dynamic cursive script recognition, a hybrid approach , 1994 .