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We begin witha handwritten grid of 32 x 40 digits on a commercial Mead Cambridge Quad Writing Pad, 8-1/2” x11”, Quad Ruled, White, 80 Sheets/Pad book with a black ink Z-Grip Series - Zebra Pen. We have authored this section in fair detail in order to aid replicability of the results. In Section 4 we present theclassification and the generative model results, and conclude the paper and discuss extended workin Section 5. In Section 3, we introduce the SAT framework. In Section 2, we cover in detail the real-world datasetgeneration process.
M a y ublished as a workshop paper at ICLR 2019 (DeepGenStruct-2019)The rest of the paper is organized as follows.
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Open-sourced scripts and code to reproduce the results disseminated in this paper. Successfully trained digit-GAN models for the Indic languages listed above.4. Thisframework can be extended in a myriad of ways to p otentially cover all digit represen-tations in various language scripts.3. The Seed-Augment-Transfer (SAT) framework for real-world digit classification. New handwritten, MNIST-styled Indic-digits datasets for the following five languages:Kannada, Tamil, Gujarati, Malayalam and Devanagari with 1280 images each.2.
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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 OpenFont License (OFL) font files freely available on the internet.The main contributions of this paper are:1. Training real-world deployment-worthy machine learning models.As seen in, deep generative m odels such as Variational Auto-Encoders (VAE) and Generative Adversarial Networks (GAN) areoften deployed during the synthesizing process. Synthesizing it using a domain specific recipe to address the reality gap 3. Generating large volumes of synthetic training data (which is often a relatively cheap pro-cess)2. TLDR : Train on synthetic fonts data, test on handwritten d i gits Transfer learning from the synthetic realm to the real-world has elicited a lot of attention in themachine learning community recently (See ).This typically entails three steps:1. This establishes notonly an interesting nexus between the font-datasets-world and transfer learningbut also provides a recipe for universal-digit classification in any script. We showcase the efficacy of this approachboth qualitatively, by training a Boundary-seeking GAN (BGAN) that generatesrealistic digit images in the five languages, and also quantitatively by testing aCNN trained on the synthetic data on the real-world datasets.
This seeddataset 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 realworld handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gu-jarati, Malayalam, and Devanagari. In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework thatcontains a synthetic seed image dataset generation procedure for languages withdifferent numeral systems using freely available open font file datasets. UnifyID AI LabsRedwood City, CA 94063, USA A BSTRACT Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri & John Whaley PPublished as a workshop paper at ICLR 2019 (DeepGenStruct-2019) F ONTS -2-HĮED -A UGMENT -T RAINFRAMEWORK FOR UNIVERSAL DIGIT CLASSIFICATION
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