Multi-Linguistic Optical Font Recognition Using Stroke Templates

Authors:
Hung-Ming Sun
Affiliations:
Kainan University, Taoyuan, Taiwan, R.O.C.
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

Citing 0
Cited 5

Farsi font recognition based on Sobel-Roberts features

Pattern Recognition Letters
New features using fractal multi-dimensions for generalized Arabic font recognition

Pattern Recognition Letters
A statistical global feature extraction method for optical font recognition

ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
A novel statistical feature extraction method for textual images: Optical font recognition

Expert Systems with Applications: An International Journal
A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the essential distinctions between different fonts is their stroke shape. A method is presented to automatically extract representative stroke templates from a text image, which contains characters of the same typeface. The collected stroke templates are classified and saved to a font database. To recognize an unknown font for an input text image, a Bayes decision rule is used to determine which font entrant in the database provides the best matching to the unknown font. The experiment demonstrates that this approach can distinguish between Chinese and English fonts without the prior information of their script. Another advantage is that it can learn a new font very quickly. Forty fonts (twenty English and twenty Chinese) are used in our experiment. An average recognition accuracy of 97 percent can be achieved in the present system.