Reliable Devanagri Handwritten Numeral Recognition using Multiple Classifier and Flexible Zoning Approach

Abstract

A reliability evaluation system for the recognition of Devanagri Numerals is proposed in this paper. Reliability of classification is very important in applications of optical character recognition. As we know that the outliers and ambiguity may affect the performance of recognition system, a rejection measure must be there for the reliable recognition of the pattern. For each character image pre-processing steps like normalization, binarization, noise removal and boundary extraction is performed. After calculating the bounding box features are extracted for each partition of the numeral image. Features are calculated on three different zoning methods. Directional feature is considered which is obtained using chain code and gradient direction quantization of the orientations. The Zoning firstly, is considered made up of uniform partitions and secondly of non-uniform compartments based on the density of the pixels. For classification 1-nearest neighbor based classifier, quadratic bayes classifier and linear bayes classifier are chosen as base classifier. The base classifiers are combined using four decision combination rules namely maximum, Median, Average and Majority Voting. The framework is used to test the reliability of recognition system against ambiguity.

A Novel Approach Towards Online Devnagari Handwritten Word Recognition Based on Robust Feature Extraction Method and FFNN Classifier.

“…Pratibha Singh calculated the features based on three different zoning methods. Directional feature is obtained using chain code and gradient direction quantization of the orientations [26]. Rajiv Kumar presented offline handwritten character recognition for Devnagari.…”

A Robust Zonal Fractal Dimension Method for the Recognition of Handwritten Telugu Digits

“…Spatial and transform domain are two major categories in Feature extraction techniques. Invariant moments [1], projections [2,64], zoning feature [3], an adaptive zoning techniques [4], [5], [55] and [6],gradient based features [7][8] [10] are some of examples of spatial domain techniques. In spatial domain statistical or structural or both [9][10][11][12][13] properties can be used for extracting features.…”