An Arabic handwriting synthesis system

• Expanding the training set for handwriting recognition can be beneficial even when the added data is synthesized from the original training data.
• Even simple shuffling-and-concatenation of characters can improve recognition accuracy.
• The usage of synthetic connection-strokes may increase the flexibility of the character-selection step.
• The recognition rate improved significantly due to our handwriting synthesis system.

In this paper, we present an Arabic handwriting synthesis system. Two concatenation models to synthesize Arabic words from segmented characters are adopted: Extended-Glyphs connection and Synthetic-Extensions connection. We use our system to synthesize handwriting from a collected dataset and inject it into an expanded dataset. We experiment by training a state-of-the-art Arabic handwriting recognition system on the collected dataset, as well as on the expanded dataset, and test it on the IFN/ENIT Arabic benchmark dataset. We show significant improvement in recognition performance due to the data that was synthesized by our system.