Data Incubation -- Synthesizing Missing Data for Handwriting Recognition
Jen-Hao Rick Chang, Martin Bresler, Youssouf Chherawala, Adrien, Delaye, Thomas Deselaers, Ryan Dixon, Oncel Tuzel

TL;DR
This paper introduces a controllable handwriting synthesizer to generate diverse training data, significantly improving online handwriting recognition accuracy by combining real and synthetic samples.
Contribution
It presents a novel framework for synthesizing handwriting data with controllable content and style, enhancing recognition performance beyond real data alone.
Findings
66% reduction in Character Error Rate
Improved recognition of underrepresented content and styles
Effective data synthesis framework for handwriting recognition
Abstract
In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recognizer from a modest amount of training samples. By training our controllable handwriting synthesizer on the same data, we can synthesize handwriting with previously underrepresented content (e.g., URLs and email addresses) and style (e.g., cursive and slanted). Moreover, we propose a framework to analyze a recognizer that is trained with a mixture of real and synthetic training data. We use the framework to optimize data synthesis and demonstrate significant improvement on handwriting recognition over a model trained on real data only. Overall, we achieve a 66% reduction in Character Error Rate.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Music and Audio Processing · Speech Recognition and Synthesis
