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

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.