Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation

• URL: http://arxiv.org/abs/2305.02577v1
• Date: Thu, 4 May 2023 06:21:00 GMT
• Title: Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation
• Authors: Renshen Wang, Yasuhisa Fujii and Alessandro Bissacco
• Abstract summary: We propose a lightweight, scalable and generalizable approach to identify text reading order. The model is language-agnostic and runs effectively across multi-language datasets. It is small enough to be deployed on virtually any platform including mobile devices.
• Score: 71.40119152422295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text reading order is a crucial aspect in the output of an OCR engine, with a large impact on downstream tasks. Its difficulty lies in the large variation of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable and generalizable approach to identify text reading order with a multi-modal, multi-task graph convolutional network (GCN) running on a sparse layout based graph. Predictions from the model provide hints of bidimensional relations among text lines and layout region structures, upon which a post-processing cluster-and-sort algorithm generates an ordered sequence of all the text lines. The model is language-agnostic and runs effectively across multi-language datasets that contain various types of images taken in uncontrolled conditions, and it is small enough to be deployed on virtually any platform including mobile devices.

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