Three-Dimensional Shape Analysis Using Moments and Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Strength of Weak Learnability
Machine Learning
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
An HMM Based Recognition Scheme for Handwritten Oriya Numerals
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Fast Zernike wavelet moments for Farsi character recognition
Image and Vision Computing
A SVM-based cursive character recognizer
Pattern Recognition
Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for fast computation of Zernike moments and their numerical stability
Image and Vision Computing
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this article, we present our recent study of offline recognition of handwritten numerals of three Indian scripts -- Devanagari, Bangla and Oriya. Here, we propose a novel approach to combination of multiple MLP classifiers with varying number of hidden nodes based on Adaboost technique. In this recognition study, we used Zernike moment features of different orders. We obtained classification results corresponding to a number of orders of this moment function and the best classification result for each script was obtained when the feature vector consists of moment values up to the order 8. It is well-known that the classification performance of an MLP largely depends on the choice of the number of hidden nodes. In the present work, we studied use of boosting as a solution to this problem of using MLP as a classifier in real-life applications. Here, we use an ensemble of MLP classifiers having different hidden layer sizes and results of their classification are combined based on Adaboost technique. Classification results have been provided using publicly available databases [1] of offline handwritten numeral images of three Indian scripts.