Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images

Understand

Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas.

• The accurate data extraction is extremely crucial in finance area.
• Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents.
• We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation.