MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary
Beiya Dai, Xing li, Qunyi Xie, Yulin Li, Xiameng Qin, Chengquan Zhang,, Kun Yao, Junyu Han

TL;DR
MataDoc is a novel document dewarping method that effectively handles arbitrary boundaries by leveraging margin and text-aware regularizations, improving OCR accuracy and document understanding.
Contribution
This paper introduces MataDoc, the first approach specifically designed for arbitrary boundary document dewarping using margin and text-aware regularizations.
Findings
Outperforms existing methods on ArbDoc benchmark with arbitrary boundaries.
Effectively preserves text line straightness in rectified images.
Demonstrates superior performance on multiple datasets like DocUNet, DIR300, and WarpDoc.
Abstract
Document dewarping from a distorted camera-captured image is of great value for OCR and document understanding. The document boundary plays an important role which is more evident than the inner region in document dewarping. Current learning-based methods mainly focus on complete boundary cases, leading to poor document correction performance of documents with incomplete boundaries. In contrast to these methods, this paper proposes MataDoc, the first method focusing on arbitrary boundary document dewarping with margin and text aware regularizations. Specifically, we design the margin regularization by explicitly considering background consistency to enhance boundary perception. Moreover, we introduce word position consistency to keep text lines straight in rectified document images. To produce a comprehensive evaluation of MataDoc, we propose a novel benchmark ArbDoc, mainly consisting…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Digital Media Forensic Detection
MethodsAttentive Walk-Aggregating Graph Neural Network · Focus
