Abstract

This paper presents a hierarchical classification of Optical Character Recognition (OCR) using global and local features with modified k-means and neural network. A hierarchical classification strategy, which contains training and recognition phases, was designed to precisely recognize the characters. In the training phase, the global features of sample characters and modified k-means clustering are employed to quickly categorize the characters into several clusters. Then each cluster and its corresponding local features of sample characters are fed into the Back-Propagation Neural Network (BPN) to learn the optimal weights. In the recognition phase, both global and local features are extracted from the test characters. Then the character is recognized by well-trained clusters’ center and neural network for coarse and fine recognition, respectively. Experimental results of the three different datasets tested showed classification rates of 97.22%, 100% and 90.28%, respectively.

Highlights

  • Optical character recognition (OCR) system is widely applied in industrial applications[1], and it has become one of the most essential applications of technology in the field of pattern recognition and artificial intelligence

  • The artificial neural network has been most frequently used as a powerful tool for classification problems

  • This paper proposes a hierarchical classification approach of OCR using global and local features with modified k-means and neural network

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Summary

Introduction

Optical character recognition (OCR) system is widely applied in industrial applications[1], and it has become one of the most essential applications of technology in the field of pattern recognition and artificial intelligence. Character recognition algorithm consists of two main stages: feature extraction and classification Both feature extraction and selection are very important in achieving high recognition performance of OCR system. Many classification techniques have been introduced[4], including statistical methods, Artificial Neural Networks (ANNs), Support Vector Machines (SVM) and multiple classifier combination. Sophisticated neural network classifiers and novel feature extraction techniques have been proposed for achieving high recognition performance[5,6,7,8]. This paper proposes a hierarchical classification approach of OCR using global and local features with modified k-means and neural network. This approach combines four different features, namely, aspect ratio, skeleton area, geometrical distance and diagonal feature.

Proposed hierarchical OCR algorithm
Feature extraction
Global feature
Local feature
Classification
Modified k-means clustering
Result of optimal k*
Experimental results x2
Findings
Conclusions

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