We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

This article presents an off-line method for recognizing handwritten digits. Structural information and quantitative features are extracted from images of isolated numerals to be classified by a hybrid multi-stage recognition system. Feature extraction starts with the raw pixel-image and derives more structured representations like line-drawings and attributed structural graphs. Classification is done in two steps: a) the structural graph is matched to prototypes, b) for each prototype there is a neural classifier which has been trained to distinguish digits having the same graph-structure. The performance of the described system is evaluated on two large databases (provided by SIEMENS AG and NIST) and is compared to other systems. Finally, the combination of the described system and a TDNN classifier is discussed. The experimental results indicate that there is an advantage in using structural information to enhance an unstructured neural classifier. 1. Introduction Automatic handwr...

Extract the Punjabi Word from image has been a very intensive area of research during last decades due to it is wide range of solution to real world problems. A lot of work has been done in languages like Chinese, Arabic, Devnagari, Urdu and English. A neural network based Gurmukhi recognition system has been developed. Range free skew detection and correction algorithms for de-skewing Gurmukhi machine printed text skewed at any angle have been developed. If different classifiers cooperate with each other group decisions may reduce errors drastically and achieve a higher performance. The whole process consists of two stages. The first, feature extraction stage analyzes the set of isolated characters and selects a set of features that can be used to uniquely identify characters. The performance depends heavily on what features are being used. Main advantage of this system is its accuracy to extract the Punjabi word. Input to the system is the scanned images from newspaper, magazines and old books and Extract the Punjabi Word from Machine printed Document Images.