DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical Documents

• URL: http://arxiv.org/abs/2404.19259v1
• Date: Tue, 30 Apr 2024 04:53:10 GMT
• Title: DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical Documents
• Authors: Taylor Archibald, Tony Martinez,
• Abstract summary: Document semantic segmentation can facilitate document analysis tasks, including OCR, form classification, and document editing. Several synthetic datasets have been developed to distinguish handwriting from printed text, but they fall short in class variety and document diversity. We propose the most comprehensive document semantic segmentation pipeline to date, incorporating preprinted text, handwriting, and document backgrounds from over 10 sources. Our customized dataset exhibits superior performance on the NAFSS benchmark, demonstrating it as a promising tool in further research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Document semantic segmentation is a promising avenue that can facilitate document analysis tasks, including optical character recognition (OCR), form classification, and document editing. Although several synthetic datasets have been developed to distinguish handwriting from printed text, they fall short in class variety and document diversity. We demonstrate the limitations of training on existing datasets when solving the National Archives Form Semantic Segmentation dataset (NAFSS), a dataset which we introduce. To address these limitations, we propose the most comprehensive document semantic segmentation synthesis pipeline to date, incorporating preprinted text, handwriting, and document backgrounds from over 10 sources to create the Document Element Layer INtegration Ensemble 8K, or DELINE8K dataset. Our customized dataset exhibits superior performance on the NAFSS benchmark, demonstrating it as a promising tool in further research. The DELINE8K dataset is available at https://github.com/Tahlor/deline8k.

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