🤖 AI Summary
To address the low accuracy and poor robustness of table row/column boundary detection under low-resolution and noisy conditions, this paper proposes a mask-based multi-scale 1D signal modeling approach. It transforms row/column boundaries into filterable 1D signals, employs progressive-variance Gaussian convolution to suppress noise, and introduces an adaptive statistical thresholding mechanism for precise structural coordinate localization. We pioneer the “mask → 1D signal” modeling paradigm, supporting zero-padding and scaling to accommodate multi-resolution inputs. Integrated into TableNet, our method achieves a Cell-Aware Segmentation Accuracy (CASA) of 76% on PubLayNet-1M—up from 67%—demonstrating substantial improvement in structural parsing performance under image degradation scenarios.
📝 Abstract
Structured data extraction from tables plays a crucial role in document image analysis for scanned documents and digital archives. Although many methods have been proposed to detect table structures and extract cell contents, accurately identifying table segment boundaries (rows and columns) remains challenging, particularly in low-resolution or noisy images. In many real-world scenarios, table data are incomplete or degraded, limiting the adaptability of transformer-based methods to noisy inputs. Mask-based edge detection techniques have shown greater robustness under such conditions, as their sensitivity can be adjusted through threshold tuning; however, existing approaches typically apply masks directly to images, leading to noise sensitivity, resolution loss, or high computational cost. This paper proposes a novel multi-scale signal-processing method for detecting table edges from table masks. Row and column transitions are modeled as one-dimensional signals and processed using Gaussian convolution with progressively increasing variances, followed by statistical thresholding to suppress noise while preserving stable structural edges. Detected signal peaks are mapped back to image coordinates to obtain accurate segment boundaries. Experimental results show that applying the proposed approach to column edge detection improves Cell-Aware Segmentation Accuracy (CASA) a layout-aware metric evaluating both textual correctness and correct cell placement from 67% to 76% on the PubLayNet-1M benchmark when using TableNet with PyTesseract OCR. The method is robust to resolution variations through zero-padding and scaling strategies and produces optimized structured tabular outputs suitable for downstream analysis.