Generative Pipeline for Data Augmentation of Unconstrained Document Images with Structural and Textural Degradation (Student Abstract)
Keywords:Computer Vision, Data Augmentation, Document Images, Handwritten Manuscripts, Generative Adversarial Networks, Optical Character Recognition, Text Image
AbstractComputer vision applications for document image understanding (DIU) such as optical character recognition, word spotting, enhancement etc. suffer from structural deformations like strike-outs and unconstrained strokes, to name a few. They also suffer from texture degradation due to blurring, aging, or blotting-spots etc. The DIU applications with deep networks are limited to constrained environment and lack diverse data with text-level and pixel-level annotation simultaneously. In this work, we propose a generative framework to produce realistic synthetic handwritten document images with simultaneous annotation of text and corresponding pixel-level spatial foreground information. The proposed approach generates realistic backgrounds with artificial handwritten texts which supplements data-augmentation in multiple unconstrained DIU systems. The proposed framework is an early work to facilitate DIU system-evaluation in both image quality and recognition performance at a go.
How to Cite
Poddar, A., Sah, A. K., Dey, S., Jawanpuria, P., Mukhopadhyay, J., & Biswas, P. K. (2023). Generative Pipeline for Data Augmentation of Unconstrained Document Images with Structural and Textural Degradation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16298-16299. https://doi.org/10.1609/aaai.v37i13.27009
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