Visual Domain Knowledge-based Multimodal Zoning for Textual Region Localization in Noisy Historical Document Images

This repository contains the implementation of zoning solution based on multimodal approach using our novel visual representation, Gravity-map. The model predicts textual regions in a set of closed polygons on a document image. The underlying CNN models used in our solution is dhSegment and FPN.

The detail of this work can be found in the corresponding paper.

Overview of Our Approach

Step-by-Step Process to Build Our Gravity-map

Step 1: Oversegment image using Voronoi-tesselation	Step 2: Compute geometric feature, gravity	Step 3: Construct Gravity-map

Resultant Samples of Zoning