ABSTRACT ADAPTIVE NEURO-FUZZY INFERENCE BASED PATTERN RECOGNITION STUDIES ON HANDWRITTEN CHARACTER IMAGES

ADAPTIVE NEURO-FUZZY INFERENCE BASED PATTERN RECOGNITION STUDIES ON HANDWRITTEN CHARACTER IMAGES This study investigates the potential use of state-space parameters and zoning based vector distance information for handwritten character modeling and the credibility of Adaptive Neuro-Fuzzy Inference System (ANFIS) for pattern recognition applications. To start with, an image database for Malayalam handwritten character set consisting of eighty one elements is designed and developed. The database consists of a total of 57,996 BMP formatted and, 128x84 pixel sized image samples collected from 716 writers. The whole set of available data is split up into two categories namely training set and test set in 1 : 1 ratio. The database developed in this work is the first of its kind in Malayalam. Among the eighty one elements present in the database, forty four basic characters are selected for the recognition studies in this thesis. In the pre-processing stage the handwritten character images are binarized using Otsu's global thresholding technique. The size normalization is also performed for uni&ing the character patterns into a standard 66x42 pixel size using Affine Transformation and Bilinear Interpolation algorithm. The size normalized binary images are then skeletonized using a rotation invariant rule based thinning algorithm. In feature extraction stage, the handwritten character images are modeled using state-space parameters extracted directly from gray-scale images with out the usual step of binarization. For this purpose, the reconstructed state-space of each character image and the scatter plot of its trajectory matrix named StateSpace Map (SSM) are generated. Then the State-Space Point Distribution (SSPD) parameters are extracted from the SSMs of the collected samples. These features are found to be promising and used later in the recognition experiments. In the next stage, the shape analysis of the Malayalam handwritten characters is performed on the basis of existence ratio, aspect ratio and the centroid of each character pattern. From this study it is found that the region decomposition or zoning is the simplest and most suitable method to identiQ the information content regions in Malayalam handwritten characters. Then the Zoned Vector Distance (Z-VD) features and Fuzzy-Zoned Normalized Vector Distance (FZ-NVD) features are extracted from the binary skeletons of the pre-processed character images. The cluster analysis based on c-Means clustering technique is performed on the extracted feature vectors and this clustering method is further used for classification purpose. The k-Nearest Neighbour statistical algorithm is also used for recognition purpose and the results indicate the credibility of the extracted features. Classification studies are also performed by means of Class-modular Multilayer Feed-forward Neural Network with Error Back-propagation learning algorithm. By comparing the recognition results obtained using the above classifiers, it is observed that the FZ-NVD parameters provide better recognition accuracy for Malayalam handwritten characters. These features are used further in the recognition experiments. The recognition results also indicate that the performance of the above used classification techniques are moderately adequate but necessitate definitely a modified combined classifier approach. In the next phase of this study, the recognition experiment is conducted based on FZ-NVD features using Adaptive Neuro-Fuzzy Inference System (ANFIS). For this purpose fuzzy If. then rules are framed from the FZ-NVD parameters. This is achieved by the fuzzification of each attribute of the feature vector using three fuzzy sets SMALL, MEDIUM and LARGE defined by Gaussian membership functions. This fuzzy rule set definitely has the capacity to handle the immense variations in distinct writing styles of individuals. In this study, we used Sugeno fuzzy model (TSK model) for modeling the inference part of ANFIS. To perform parameter adjustments in learning, ANFIS needs partitioning of the input space according to the target classes. In order to facilitate this process a pre-processing operation is performed on the entire rule set using ID3 decision tree induction procedure. The reduced set of optimum rules obtained as a result of this pre-processing is given as input to the inference part of ANFIS. A gradient based learning algorithm, proposed by Jang.J-S.R is effectively used to estimate the parameter set in