Organizational Learning In Chinese Character Recognition And Image Analysis Applications

Human efficiency in real world signal processing and pattern recognition tasks has been enthralling and far-away from modern computer practice for ages. While biological and psychophysical endeavors are exploring the complex mechanism in human perception, a number of artificial neural network models have been built to enhance the performance of computer systems in real world understanding and adaptation.Among these network models implementation with supervised learning algorithms, such as Back-propagation, has been quite successful so far, yet substantial draw-backs limit their wider application in solving complex problems. In this thesis we pay special interest in a school of self-organized constructions. These models, bearing more biological plausibility, are oriented to feature extraction, clustering, and topological mapping of input data, and all these characteristics are achieved without guidance of a teacher. Another issue of interest is the early vision model, of which, thanks to biological and psychophysical discoveries, have become relatively well-known. The main achievement in this thesis, are based on both of the two topics we mentioned above.We first test the ability of a PCA network, based on unsupervised Hebbian learning, in image segmentation. Nonlinear mechanism is introduced to enhance the convergence speed of network training.Apart from conventional methods such as structure analysis, we try to tackle the problem of handwritten Chinese chiracter recognition from an early-vision point of view. A set of Gabor filters, analogous to simple cells in visual cortex. are used to extract local orientational and spatial frequency features occurred in the character image. These features are further piped into a Self-Organized Mapping (SOM) process to be clustered, and the mapping result is learned by a supervised algorithm LVQ so that good recognition result is obtained.We also proposed a network model for texture segmentation, also based on SOM and Gabor filtering.Finally, a computational mode! for the problem of visual receptive field learning is constructed. A multi-resolution image pyramid, originally used for image analysis, is applied to provide spatial frequency information into the learning process, which we further strengthen with a new Self-Organized Hebbian Learning algorithm. As a result, receptive fields with both orientation and spatial frequency selectivity, similar to Gabor filters, are learned and tested.