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We propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets

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DeepGenStruct-Notebooks

TLDR: Fonts constitute an awesome, free and untapped resource for training text image recognition models. Train on synthetic data (fonts) and transfer to handwritten digits.

Citation:

Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri and John Whaley, Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification , Proceedings, ICLR 2019 Workshop on Deep Generative Models for Highly Structured Data, May 2019, New Orleans, USA

BibTex:

@InProceedings{Prabhu2019SAT, author = {Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri and John Whaley}, journal = {Proceedings, ICLR 2019 Workshop on Deep Generative Models for Highly Structured Data }, pages = {1--11}, title = { Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification }, year = {2019} }

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We propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets

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