Synthetic Data Augmentation for Table Detection: Re-evaluating TableNet's Performance with Automatically Generated Document Images
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Abstract (arXiv)
Document pages captured by smartphones or scanners often contain tables, yet manual extraction is slow and error-prone. We introduce an automated LaTeX-based pipeline that synthesizes realistic two-column pages with visually diverse table layouts and aligned ground-truth masks. The generated corpus augments the real-world Marmot benchmark and enables a systematic resolution study of TableNet. Training TableNet on our synthetic data achieves a pixel-wise XOR error of 4.04% on our synthetic test set with a 256x256 input resolution, and 4.33% with 1024x1024. The best performance on the Marmot benchmark is 9.18% (at 256x256), while cutting manual annotation effort through automation.
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References · 8
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2017Mask R-CNNcited by 7,277
- 2020DocBank: A Benchmark Dataset for Document Layout Analysiscited by 153
- 2021PubTables-1M: Towards comprehensive table extraction from unstructured documentscited by 126
- 2021A Survey on Data Augmentation for Text Classificationcited by 111
- 2022TableFormer: Robust Transformer Modeling for Table-Text Encodingcited by 91
- 2020TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Imagescited by 69
- 2018A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documentscited by 15
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