Vector-to-Image Transformation of Character Patterns for On-line and Off-line Recognition.

Saved in:

Bibliographic Details
Title:	Vector-to-Image Transformation of Character Patterns for On-line and Off-line Recognition.
Authors:	Velek, Ondřej, Nakagawa, Masaki, Liu, Cheng-Lin
Source:	International Journal of Computer Processing of Oriental Languages. Jun2002, Vol. 15 Issue 2, p187. 23p.
Subjects:	Character sets (Data processing), Pattern recognition systems
Abstract:	This paper proposes a method to generate realistic character images from on-line patterns. From the pen trajectory of an on-line pattern, the proposed method generates images of various stroke shapes using four painting modes: constant line mode, proportional mode and two calligraphic modes. Particularly, the calligraphic modes combine the pen trajectory with real stroke-shape images so that the generated images resemble the characters produced with brush pen. In the calligraphic painting mode based on primitive stroke identification (PSI), the strokes of on-line patterns are classified into different classes and each class of strokes is painted with the corresponding stroke shape template; while in the calligraphic painting mode based on stroke component classification (SCC), each stroke is decomposed into ending, bending and connecting parts, and each part is painted with a stroke shape template. Decomposing strokes into parts is helpful to deal with connected strokes in cursive writing. Our method of image transformation serves two purposes: supplying image samples for off-line recognition, and application of off-line recognition methods to on-line recognition. The experimental results show that our methods are well suited for both purposes. We show that calligraphic painting mode is appropriate for offline recognition while constant line mode and proportional mode are appropriate for on-line recognition. [ABSTRACT FROM AUTHOR]
	Copyright of International Journal of Computer Processing of Oriental Languages is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	This paper proposes a method to generate realistic character images from on-line patterns. From the pen trajectory of an on-line pattern, the proposed method generates images of various stroke shapes using four painting modes: constant line mode, proportional mode and two calligraphic modes. Particularly, the calligraphic modes combine the pen trajectory with real stroke-shape images so that the generated images resemble the characters produced with brush pen. In the calligraphic painting mode based on primitive stroke identification (PSI), the strokes of on-line patterns are classified into different classes and each class of strokes is painted with the corresponding stroke shape template; while in the calligraphic painting mode based on stroke component classification (SCC), each stroke is decomposed into ending, bending and connecting parts, and each part is painted with a stroke shape template. Decomposing strokes into parts is helpful to deal with connected strokes in cursive writing. Our method of image transformation serves two purposes: supplying image samples for off-line recognition, and application of off-line recognition methods to on-line recognition. The experimental results show that our methods are well suited for both purposes. We show that calligraphic painting mode is appropriate for offline recognition while constant line mode and proportional mode are appropriate for on-line recognition. [ABSTRACT FROM AUTHOR]
ISSN:	02194279