A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding

TL;DR

This paper presents a novel ensemble approach using Error Correcting Output Coding combined with feature selection to improve Persian handwritten letter recognition accuracy, demonstrating superior results over existing methods.

Contribution

The study introduces an ECOC ensemble method with feature selection for Persian handwritten letter recognition, enhancing accuracy and reducing error costs compared to prior techniques.

Findings

ECOC ensemble outperforms other methods and single classifiers.

Feature selection reduces error costs in the recognition process.

Support Vector Machine effectively classifies Persian handwritten letters.

Abstract

Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemble method. Furthermore, the feature selection is used to reduce the costs of errors in our proposed method. ECOC is a method for decomposing a multi-way classification problem into many binary classification tasks; and then combining the results of the subtasks into a hypothesized solution to the original problem. Firstly, the image features are extracted by Principal Components Analysis (PCA). After that, ECOC is used for identification the Persian handwritten letters which it uses Support Vector Machine (SVM) as the base classifier. The empirical results of applying this ensemble method using 10 real-world data sets of Persian handwritten letters indicate that this method has better results in identifying the Persian handwritten letters than other ensemble methods and also single classifications. Moreover, by testing a number of different features, this paper found that we can reduce the additional cost in feature selection stage by using this method.