Handwritten recognition with multiple classifiers for restricted lexicon

This paper presents a multiple classifier system applied to the handwritten word recognition (HWR) problem. The goal is to analyse the influence of different global classifiers taken in isolation as well as combined in a particular HWR task. The application proposed is the recognition of the Portuguese handwritten names of the months. The strategy takes advantage of the complementary mechanisms of three different classifiers: conventional neural network, class-modular neural network and hidden Markov models, yielding a multiple classifier that is more efficient than either individual technique. The recognition rates obtained vary from 75.9% using the standalone HMM classifier to 96.0% considering the classifier combination.

[1]  Anil K. Jain,et al.  Feature extraction methods for character recognition-A survey , 1996, Pattern Recognit..

[2]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[3]  Lambertus Schomaker.  A method for the determination of features used in human reading of cursive handwriting. , 1998 .

[4]  Venu Govindaraju,et al.  The Role of Holistic Paradigms in Handwritten Word Recognition , 2009 .

[5]  Cinthia Obladen de Almendra Freitas,et al.  Handwritten isolated word recognition: an approach based on Mutual Information for feature set validation , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[6]  Cinthia Obladen de Almendra Freitas,et al.  Evaluating the conventional and class-modular architectures feedforward neural network for handwritten word recognition , 2003, 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003).

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

[8]  Andreas Zell,et al.  SNNS (Stuttgart Neural Network Simulator) , 1994 .

[9]  Cinthia Obladen de Almendra Freitas,et al.  Evaluating NN and HMM classifiers for handwritten word recognition , 2002, Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing.

[10]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[11]  Flávio Bortolozzi,et al.  Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach , 2003, Document Analysis and Recognition.

[12]  Jinho Kim,et al.  A methodology of combining HMM and MLP classifiers for cursive word recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  Robert Sabourin,et al.  An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Ching Y. Suen,et al.  A class-modular feedforward neural network for handwriting recognition , 2002, Pattern Recognit..