Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation
This addresses the challenge of text reading order for OCR applications in real-world scenarios with varied layouts and image degradations, representing an incremental improvement.
The paper tackles the problem of determining text reading order in OCR under uncontrolled conditions, proposing a lightweight GCN-based method that achieves effective performance across multi-language datasets and is deployable on mobile devices.
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.