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. This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also qualitatively by testing a CNN trained on the synthetic data on the real-world datasets. This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script. Transfer learning from the synthetic realm to the real-world has elicited a lot of attention in the machine learning community recently (See [1; 2; 3]), which typically entails three steps: generating large volumes of synthetic training data (which is often a relatively cheap process), synthesizing it using a domain specific recipe to address the reality gap BID3 , and training real-world deploymentworthy machine learning models. As seen in [1; 2; 3] , deep generative models such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) are often deployed during the synthesizing process. This paper fits squarely into this category of work, where we tackle the problem of absence of MNIST-scale datasets for Indic scripts to achieve high, real-world accuracy digit classification by using synthetic datasets generated by harnessing the Open Font License 1 (OFL) font files freely available on the internet.We begin with a handwritten grid of 32 x 40 digits on a commercial Mead Cambridge Quad Writing Pad, 8-1/2" x 11", Quad Ruled, White, 80 Sheets/Pad book with a black ink Z-Grip Series -Zebra Pen. We then scan the sheet(s) using a Dell -S3845cdn scanner BID2 and use an image segmentation script to slice the scanned image grid to generate 1280 28 x 28 mnist-ized digit images. An example of the raw scanned image for the Devanagri-Hindi script is as shown in FIG1 We now perform a novel 2-step sanity check for MNIST compatibility for the languages in the following manner. We introduced 5 new real-world digit datasets in Indic languages and also a transfer learning framework which we term as Seed-Augment-Train/Transfer (SAT) to perform real-world handwritten digit classification in these languages. Our goal is to draw the attention of the machine learning community to this potentially attractive nexus between the work happening in the synthetic-to-real transfer learning domain and a veritably rich reservoir of semi-synthetic textual glyphs training data freely available in the form of Font files.We showcased the efficacy of this SAT-based approach by perform digit-classification and also training GANs for the digits of the languages considered. We are expanding this foundational effort in several directions: the first entails using a small portion of the dataset for transfer learning, which will help tackle cases in which off-the-shelf image augmenters do not effectively capture human handwriting's natural variations. The second involves generative modeling of the deviations in handwritten digits with regards to the clean seed-synthetic dataset using the extended MNIST dataset (in lieu of the digits themselves). We would then apply this approach to deep augment seed datasets
Is seeding and augmentation all you need for classifying digits in any language? This paper presents new datasets for five languages and proposes a new framework (SAT) for font image datasets generation for universal digit classification.