Use of Positional Information in Sequence Alignment for Multiple Classifier Combination

There are problems in pattern recognition where the output of a system is a sequence of classes rather than a single class. A well-known example is handwritten sentence recognition. In order to make those problems amenable to classifier combination techniques, an algorithm for sequence alignment must be provided. The present paper describes such an algorithm. The algorithm extends an earlier method by including information about the location of each pattern in a sequence. The proposed approach is evaluated in the context of a system for handwritten sentence recognition. It is demonstrated through experiments that by the use of positional information the computationally expensive process of multiple sequence alignment can be significantly sped up without loosing recognition accuracy.

[1]  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).

[2]  Horst Bunke,et al.  Handwritten sentence recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Kazuhito Murakami,et al.  High speed line detection by Hough transform in local area , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Teuvo Kohonen,et al.  Median strings , 1985, Pattern Recognit. Lett..

[6]  Ching Y. Suen,et al.  Combination of multiple classifier decisions for optical character recognition , 1997 .

[7]  Michael J. Fischer,et al.  The String-to-String Correction Problem , 1974, JACM.

[8]  J. Kruskal An Overview of Sequence Comparison: Time Warps, String Edits, and Macromolecules , 1983 .

[9]  Horst Bunke,et al.  Using a Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System , 2001, Int. J. Pattern Recognit. Artif. Intell..

[10]  Gyeonghwan Kim,et al.  An architecture for handwritten text recognition systems , 1999, International Journal on Document Analysis and Recognition.

[11]  Daniel P. Lopresti,et al.  Using Consensus Sequence Voting to Correct OCR Errors , 1997, Comput. Vis. Image Underst..

[12]  Robert P. W. Duin,et al.  Experiments with Classifier Combining Rules , 2000, Multiple Classifier Systems.

[13]  Leonardo Maria Reyneri,et al.  Beatrix: A self-learning system for off-line recognition of handwritten texts , 1997, Pattern Recognit. Lett..