Ensembles of classifiers for handwritten word recognition

Abstract.Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation and combination methods, known as ensemble methods, have been proposed in the field of machine learning. They have shown improved recognition performance over single classifiers. In this paper the application of some of those ensemble methods in the domain of offline cursive handwritten word recognition is described. The basic word recognizers are given by hidden Markov models (HMMs). It is demonstrated through experiments that ensemble methods have the potential of improving recognition accuracy also in the domain of handwriting recognition.

[1]  Lee Luan Ling,et al.  Disconnected handwritten numeral image recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[2]  A. Brakensiek,et al.  OFF-LINE HANDWRITING RECOGNITION USING VARIOUS HYBRID MODELING TECHNIQUES AND CHARACTER N-GRAMS , 2004 .

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

[4]  Jay J. Lee,et al.  Data-Driven Design of HMM Topology for Online Handwriting Recognition , 2001, Int. J. Pattern Recognit. Artif. Intell..

[5]  Giuseppe Pirlo,et al.  A PERTURBATION-BASED APPROACH FOR MULTI-CLASSIFIER SYSTEM DESIGN , 2004 .

[6]  JinHyung Kim,et al.  Data-driven Design of HMM Topology for On-line Handwriting Recognition , 2000 .

[7]  Stephan Baumann,et al.  Advances in Document Classification by Voting of Competitive Approaches , 1996, DAS.

[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]  William B. Yates,et al.  Engineering Multiversion Neural-Net Systems , 1996, Neural Computation.

[10]  Horst Bunke,et al.  Automatic segmentation of the IAM off-line database for handwritten English text , 2002, Object recognition supported by user interaction for service robots.

[11]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Ching Y. Suen,et al.  Multiple Classifier Combination Methodologies for Different Output Levels , 2000, Multiple Classifier Systems.

[13]  Samy Bengio,et al.  Offline recognition of unconstrained handwritten texts using HMMs and statistical language models , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[15]  Rolf-Dieter Bippus.  1-dimensional and pseudo 2-dimensional HMMs for the recognition of German literal amounts , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[16]  Patrick Gallinari,et al.  An hybrid MLP-SVM handwritten digit recognizer , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

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

[18]  Horst Bunke,et al.  Lexicon reduction in an framework based on quantized feature vectors , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[19]  Samy Bengio,et al.  Offline cursive word recognition using continuous density hidden Markov models trained with PCA or ICA features , 2002, Object recognition supported by user interaction for service robots.

[20]  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..

[21]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Josef Kittler,et al.  Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings , 2000 .

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

[24]  Gerhard Rigoll,et al.  Multi-branch and two-pass HMM modeling approaches for off-line cursive handwriting recognition , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[25]  Josef Kittler,et al.  Experimental evaluation of expert fusion strategies , 1999, Pattern Recognit. Lett..

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

[27]  Torsten Caesar,et al.  Sophisticated topology of hidden Markov models for cursive script recognition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[28]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[29]  Leo Breiman,et al.  HALF&HALF BAGGING AND HARD BOUNDARY POINTS , 1998 .

[30]  Jianzhong Wu,et al.  An implementation of postal numerals segmentation and recognition system for Chinese business letters , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[31]  Gilles F. Houle,et al.  A Multi-Layered Corroboration-Based Check Reader , 1996, DAS.

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

[33]  AlgorithmsThomas,et al.  Machine Learning Bias , Statistical Bias , andStatistical Variance of Decision Tree , 1995 .

[34]  Horst Bunke,et al.  Automatic bankcheck processing , 1997 .

[35]  Ching Y. Suen,et al.  A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[37]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[38]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[39]  K. Maruyama,et al.  RECOGNITION METHOD FOR CURSIVE JAPANESE WORD WRITTEN IN LATIN CHARACTERS , 2004 .

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

[41]  Makoto Kobayashi,et al.  Off-line character recognition using HMM by multiple directional feature extraction and voting with bagging algorithm , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).