Scope: Table detection and table structure recognition

This paper: CascadeTabNet, single deep learning-based end-to-end model for two problems solved at once


  • Cascade R-CNN (Cai and Vasconcelos, 2018) is a multistage model for detection
  • HRNet (Wang et al, 2019) for semantic segmentation and object detection
  • Foundings: Cascaded multistaged model with HRNet backbone yields the best result
  • Network (implemented using mmdetection)
    • Image feed to HRNetV2p_W32 to produce feature map
    • RPN (region proposal network) takes feature map and predicts object proposals (B0, region of interest, RoI)
      • B0 = boudning box classification scores + box regression points
    • BBox takes RoI B0 and feature map and makes predictions (B1)
    • Another BBox heads take B1 and feature map to produce B2, and B2 to produce B3 (final bounding box)
    • A mask layer takes B2 and feature map to produce masks for objects (S, segmentation output)
      • mmdetection: cascade_mask_rcnn_hrnetv2p_w32_20e
    • Branching at output of B2: Produces B3 and S (from segmentation branch)

Transfer learning in 2 stages

  1. General task of table detection: Using general dataset
    • images of different types of documents, containing tables of various types
    • goal: detect tables in images
  2. Detect tables according to table types (bordered, semi-bordered, borderless)
    • two-stage transfer learning strategy helps to use only small amount of data

First iteration

  • start with CNN model with pretrained ImageNet COCO model weights
  • learn only task-specific high level features
  • result: predicts the detection masks for table in images

Second iteration

  • model fine-tuned on smaller dataset for specific tasks
    • predicting cell masks in borderless tables
    • detecting table types
  • need smaller data set with advanced annotations
  • semibordered and borderless tables share the same class because the border is not helpful

Image augmentation techniques

  • for generating training data, and avoid overfitting
  • digital documents in datasets are perfectly axis-aligned
    • should not do shear and rotation transform
  • dilation transform: Thicken the black pixel regions
    • convert into binary image first
    • use a 2x2 kernel filter with one iteration to do the transform
  • smudge transform: Spread black pixel to create smeary blurred black pixel region
    • implemented using various distance transforms
    • algorithm: Gilani et al (2017), using Euclidean distance transform, linear distance transform, max distance transform


  • CascadeTabNet take images containing zero or more tables, predicts segmentation masks for tables (bordered and borderless)
  • Branches: Bordered and Borderless
    • Borderless: Predict cells, then arrange into rows nad columns based on their positions. Detect cells using contour-based text detection algorithm
    • Bordered: Using line detection algorithm. No cell segmentation as line information is easier


  • General dataset is merge of 3:
    1. ICDAR19 (cTDaR)
    2. Marmot
  • Removed some ground truth errors in dataset
  • Create 4 training sets
    1. original images
    2. original + dilate transform
    3. smudge transform
    4. original + dilate + smudge
  • Evaluation using tablebank dataset

Bibliographic data

   title = "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents",
   author = "Devashish Prasad and Ayan Gadpal and Kshitij Kapadni and Manish Visave and Kavita Sultanpure",
   booktitle = "Proceedings of CVPR2020 Workshop on Text and Documents in the Deep Learning Era",
   year = "2020",
   address = "Seattle, WA",