Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)

• Published in: IEEE transactions on neural networks Vol. 10; no. 4; pp. 939 - 945
• Main Authors: Bailing Zhang, Minyue Fu, Hong Yan, Jabri, M.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 1999; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Artificial intelligence; Classification; Computer science; control theory; systems; Connectionism. Neural networks; Construction; Data structures; Digits; Exact sciences and technology; Handwriting recognition; Learning systems; Mathematical models; Modules; Neural networks; Nonhomogeneous media; Numerical stability; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Principal component analysis; Recognition; System testing; Vectors; Hand writing; Self organization; Adaptive system; Printed circuit board; Digital circuit; Experimental study; Recognition; Principal component analysis
• ISSN: 1045-9227
• DOI: 10.1109/72.774267

The adaptive-subspace self-organizing map (ASSOM) proposed by Kohonen is a recent development in self-organizing map (SOM) computation. In this paper, we propose a method to realize ASSOM using a neural learning algorithm in nonlinear autoencoder networks. Our method has the advantage of numerical stability. We have applied our ASSOM model to build a modular classification system for handwritten digit recognition. Ten ASSOM modules are used to capture different features in the ten classes of digits. When a test digit is presented to all the modules, each module provides a reconstructed pattern and the system outputs a class label by comparing the ten reconstruction errors. Our experiments show promising results. For relatively small size modules, the classification accuracy reaches 99.3% on the training set and over 97% on the testing set.