An improvement over template matching using K-means algorithm for printed cursive script recognition
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
An improvement over template matching using k-means algorithm for printed cursive script recognition
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
| Hi-index | 0.00 |
In recent years functional decomposition methods, widely known to logic synthesis researchers are being applied in diverse fields such as Machine Learning [14] [17], Knowledge Discovery [16] [17], Information Systems [5] [7] [13] and Image Compression [10]. This paper presents a novel method for recognising machine printed characters and character images using functional decomposition. The methods found in literature [9] [20] try to find some characteristics of an image and apply different computational intelligence techniques to match them to a character. Each character or image is viewed as set of conditions with a corresponding set of decisions. This paper shows functional decomposition [4] [6] as a tool in determining the characters by considering less number of conditions. Moreover, the structures produced by functional decomposition are easily implementable by FPGAs and therefore can be quickly reconfigured to suit a different set of characters. The problem of character recognition is analogous to decision making in information systems. The decision table (DT) generated from a set of characters is functionally decomposed and intermediate decision rules are generated. The advantage of the proposed method is in the analysis of the character recognition process using the intermediate conditions and decisions.