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In this paper, 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.
The indian origin of the modern place-value arithmetical notation
Sāradākānta Gāṅguli · 1932
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Eder Santana and George Hotz · 2016
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Data Mining: Practical machine learning tools and techniques
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https://pillow.readthedocs.io/en/stable/
Cited in the paper.
Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris, and Sakyasingha Dasgupta
Cited in the paper.
Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb · 2017
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Transfer learning from synthetic to real images using variational autoencoders for precise position detection
Tadanobu Inoue, Subhajit Choudhury, Giovanni De Magistris, and Sakyasingha Dasgupta · 2018
Later among the works it cites.
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