Design and Development of a Framework For Stroke-Based Handwritten Gujarati Font Generation
This addresses the problem of preserving cultural heritage and enabling personalized designs for users of the Gujarati script, though it is incremental as it applies existing stroke-based methods to a specific script.
The paper tackled generating handwritten fonts for the Gujarati script by developing a stroke-based framework that learns rules from character strokes and synthesizes remaining glyphs from a user-provided subset, achieving 84.84% accuracy in subjective evaluation and 84.28% in OCR evaluation.
Handwritten font generation is important for preserving cultural heritage and creating personalized designs. It adds an authentic and expressive touch to printed materials, making them visually appealing and establishing a stronger connection with the audience. This paper aims to design a framework for generating handwritten fonts in the Gujarati script, mimicking the variation of human handwriting. The proposed font generation model consists of a learning phase and a generation phase. In the learning phase, Gujarati scripts are analyzed, and rules for designing each character are formulated. This ruleset involves the concatenation of strokes in a stroke-based manner, ensuring visual consistency in the resulting glyphs. The generation phase involves the user providing a small subset of characters, and the system automatically generates the remaining character glyphs based on extracted strokes and learned rules, resulting in handwritten Gujarati fonts. The resulting character glyphs are converted into an open-type font using the FontForge tool, making them compatible with any Gujarati editor. Both subjective and objective evaluations are conducted to assess the synthesized images and fonts. Subjective evaluation through user studies provides feedback on quality and visual appeal, achieving an overall accuracy of 84.84%. Notably, eleven characters demonstrated a success ratio above 90%. Objective evaluation using an existing recognition system achieves an overall accuracy of 84.28% in OCR evaluation. Notably, fifteen characters had a success ratio of 80% or higher.