Post-Processing Mask-Based Table Segmentation for Structural Coordinate Extraction

TL;DR

This paper introduces a multi-scale signal-processing approach for robust table segment boundary detection in noisy, low-resolution images, enhancing structured data extraction accuracy from document images.

Contribution

It proposes a novel multi-scale Gaussian convolution method for detecting table edges from masks, improving robustness and accuracy over existing image-based approaches.

Findings

Improved CASA score from 67% to 76% on PubLayNet-1M.

Enhanced robustness to resolution variations.

Effective suppression of noise while preserving structural edges.

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.