An Approach for Arabic Handwriting Synthesis Based on Active Shape Models

Comprehensive handwriting databases are crucial to train and test script recognition systems. However their generation is expensive in sense of manpower and time. As a result there is a lack of such databases which impedes research and development. This is especially true in case of holistic word recognition, since various samples must be available for each entry of the underlying vocabulary. To bypass this problem for Arabic, we present an efficient system that automatically generates images of synthetic handwritten words or text lines from unicode. A total of 28046 online samples of multiple writers are created to compute Active Shape Models (ASM) for over hundred letter classes. ASMs are used to generate unique letter representations for each synthesis. Subsequently these representations are modified by affine transformations, smoothed by B-Spline interpolation and composed to text. Finally the text is rendered and saved. In this way our system produces off-line pseudo handwritten samples with variations in shape and texture. We compare samples of the IFN/ENIT database with corresponding syntheses to show that these can be used to surrogate real samples.