Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor

Author

Yang, Su

Author_Institution

Dept. of Comput. Sci. & Eng., Fudan Univ., Shanghai, China

Volume

27

Issue

2

fYear

2005

Firstpage

278

Lastpage

281

Abstract

A new descriptor for symbol recognition is proposed. 1) A histogram is constructed for every pixel to figure out the distribution of the constraints among the other pixels. 2) All the histograms are statistically integrated to form a feature vector with fixed dimension. The robustness and invariance were experimentally confirmed.

Keywords

character recognition; constraint theory; feature extraction; image recognition; statistical analysis; feature vector; pixel level constraint histograms; statistical integration; symbol recognition; Character recognition; Circuits; Engineering drawings; Feature extraction; Graphics; Histograms; Optical distortion; Optical noise; Robustness; Shape; Index Terms- Symbol recognition; descriptor; feature extraction; feature representation.; graphics recognition; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Statistical; Numerical Analysis, Computer-Assisted; Patents as Topic; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2005.38

Filename

1374874