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Improving table detection for document images using boundary
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DOI：10.1007/s40747-023-01235-9.png)
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Abstract
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Locating tables in document images is the first step to extracting table information, and high location precision is required. The dominant approach of table detection is based on an object detection algorithm, and the detector defines the prediction task as a regression problem, which inevitably leads to positioning errors. To address this issue, this paper presents an approach called Border Line Correction (BLC) to refine the rough prediction results of the original detector through the table boundary lines extracted from the document image. Our approach transforms the regression task into a classification problem, thus avoiding the inherent regression error of the object detection algorithm. Traditional annotation methods are inadequate for table detection tasks as they fail to capture the completeness and purity of the detection results. Therefore, this study treats the correct position of a table as a tolerance region. Additionally, to overcome the limitations of existing datasets in the materials domain, we collected 1183 samples from scientific literature in the materials field and created the MatTab dataset, annotating the tables with tolerance regions. This paper use Cascade RCNN with Swin Transformer as baseline models, and BLC is utilized to optimize the detection results. Experimental results demonstrate significant improvements with BLC at an IOU of 0.95 on the MatTab, ICDAR2019, and ICDAR2017 datasets. In MatTab, the percentage of correctly detected complete and pure tables increased from 72.3% to 82.1%.
Keywords:
Table detection
Deep learning
Document image
Object detection
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