Classifiers fusion method to recognize handwritten persian numerals

TL;DR

This paper introduces a classifier fusion approach combining KNN, LC, and SVM to improve recognition accuracy of handwritten Persian numerals, achieving nearly 100% accuracy on a large dataset.

Contribution

It proposes a novel classifier fusion method using a strong feature set to enhance recognition accuracy with fewer features.

Findings

Achieved approximately 99.90% recognition accuracy on 5,000 test samples.

Obtained 99.97% accuracy using four-fold cross-validation.

Demonstrated effectiveness of classifier fusion in Persian numeral recognition.

Abstract

Recognition of Persian handwritten characters has been considered as a significant field of research for the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to increase the recognition percentage. For implementing the classifier fusion technique, we have considered k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The innovation of this tactic is to attain better precision with few features using classifier fusion method. For evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples, and the correct recognition ratio achieved approximately 99.90%. Additional, we got 99.97% exactness using four-fold cross validation procedure on 20,000 databases.