OBJECT ORIENTATION DETECTION AND CHARACTER RECOGNITION USING OPTIMAL FEEDFORWARD NETWORK AND KOHONEN'S FEATURE MAP

摘要

A neural network model, namely, Kohonen's Feature Map, together with the Optimal Feedforward Network are used for variable font machine printed character recognition with tolerance to rotation, shift in position and size errors. The determination of object orientation is found using the many rotated versions of individual symbols. Orientations are detected from printed text but no knowledge of the context is used. The optimal Bayesian detector is derived and it is shown that the optimal detector has the form of a feedforward network. This network together with the Learning Vector Quantization (LVQ) approach are able to implement an inspection system which determines the orientation of the fonts. After the size normalization, rotation and component finding process as a preprocessing step, the text becomes the input for the Feture Map. Feature Map is trained first in an unsupervised manner. The algorithm is then adapted for supervised learning using improved LVQ technique. Rectangular and Minimal Spanning Tree (MST) neighborhod topologies are experimented with. The results are encouraging, where 87 % of the characters of various fonts are correctly recognized even though the pattern is distorted in shape and transformed in a shift, size and ratation invariant manner. Experimantal results and comparisons are described.