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.
Abstract (translated)
从表格中提取结构化数据在扫描文档和数字档案的图像分析中起着关键作用。尽管已经提出了许多方法来检测表结构并提取单元格内容，但在低分辨率或噪声图像中准确识别表格段边界（行和列）仍然具有挑战性。在很多现实场景中，由于表格数据不完整或退化，基于变压器的方法对噪声输入的适应能力受到限制。基于掩码的边缘检测技术在这种情况下表现出更强的鲁棒性，因为它们可以通过调整阈值来改变敏感度；然而，现有的方法通常直接将掩码应用于图像，导致了对噪声的敏感性、分辨率损失或计算成本高昂的问题。 本文提出了一种新的多尺度信号处理方法，用于从表格掩码中检测表格边缘。行和列转换被建模为一维信号，并通过使用逐渐增加方差的高斯卷积进行处理，随后采用统计阈值来抑制噪声并保留稳定的结构边缘。检测到的信号峰值会被映射回图像坐标以获得准确的段边界。 实验结果表明，在使用TableNet与PyTesseract OCR时，该方法应用于列边缘检测可以将布局感知度量（Cell-Aware Segmentation Accuracy, CASA）从67%提高至76%，CASA同时评估文本正确性和正确的单元格放置。此外，通过零填充和缩放策略，所提出的方法对分辨率变化具有鲁棒性，并生成适用于下游分析的优化结构化表格输出。
URL
https://arxiv.org/abs/2512.21287
