Sparse representation based on K-nearest neighbor classifier for degraded Chinese character recognition

Authors:
Long Ma;Baihua Xiao;Chunheng Wang
Affiliations:
Key Laboratory of Complex System and Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex System and Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex System and Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Year:
2010

Citing 14
Cited 0

A Statistical, Nonparametric Methodology for Document Degradation Model Validation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Gray-Scale Extraction of Features for Character Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Extraction of Topographic Features for Gray Scale Character Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten character recognition using elastic matching based on a class-dependent deformation model

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Low resolution character recognition by dual eigenspace and synthetic degraded patterns

Proceedings of the 1st ACM workshop on Hardcopy document processing
Camera based Degraded Text Recognition Using Grayscale Feature

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Low Resolution Character Recognition by Image Quality Evaluation

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Degraded Character Recognition by Complementary Classifiers Combination

ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Robust reconstruction of low-resolution document images by exploiting repetitive character behaviour

International Journal on Document Analysis and Recognition
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit

Foundations of Computational Mathematics
Recognition of very low-resolution characters from motion images captured by a portable digital camera

PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

IEEE Transactions on Information Theory
Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an effective coarse-to-fine algorithm to recognize the degraded Chinese characters. The algorithm contains two basic steps. Firstly, for the test images and the train images, reduce the dimension of the character feature via principal component analysis (PCA), and K-nearest neighbor classifier is exploited to find the candidate recognition results. Secondly, a sparse representation algorithm is explored as a fine recognition classifier. A dictionary is constructed by the PCA feature spaces of all the training images of the candidates' categories to reconstruct the input image via sparse representation, and the residual error is calculated by the sparse coefficients corresponding to each candidate category. We apply the method to the low resolution and noised 3755 categories of Chinese characters, the comparison experiments verify the efficacy of the proposed algorithm.