Variational Augmentation for Enhancing Historical Document Image Binarization
Avirup Dey, Nibaran Das, Mita Nasipuri

TL;DR
This paper introduces a two-stage deep learning framework that uses variational inference to generate degraded training samples, significantly improving historical document image binarization performance on challenging datasets.
Contribution
The novel two-stage framework combines variational inference-based data augmentation with CNN binarization, addressing data scarcity in historical document processing.
Findings
Achieved competitive results on DIBCO datasets
Outperformed several existing binarization methods
Demonstrated robustness on severely degraded images
Abstract
Historical Document Image Binarization is a well-known segmentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework -- the first of which comprises a generator that generates degraded samples using variational inference and the second being a CNN-based binarization network that trains on the generated data. We evaluated our framework on a range of DIBCO datasets, where it achieved competitive results against previous state-of-the-art methods.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · COVID-19 diagnosis using AI
MethodsVariational Inference
