StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks

TL;DR

StrokeNet introduces a novel approach for scene text detection by focusing on stroke-level localization and hierarchical relational reasoning, achieving state-of-the-art results on multiple benchmarks.

Contribution

The paper proposes StrokeNet, which uses stroke-assisted prediction and hierarchical graph reasoning for improved text detection, along with a new stroke-level annotated dataset, SynthStroke.

Findings

Achieves state-of-the-art performance on multiple benchmarks.

Effectively splits close text instances and detects arbitrary-shaped texts.

Introduces a new dataset with stroke-level annotations for training.

Abstract

Scene text detection is still a challenging task, as there may be extremely small or low-resolution strokes, and close or arbitrary-shaped texts. In this paper, StrokeNet is proposed to effectively detect the texts by capturing the fine-grained strokes, and infer structural relations between the hierarchical representation in the graph. Different from existing approaches that represent the text area by a series of points or rectangular boxes, we directly localize strokes of each text instance through Stroke Assisted Prediction Network (SAPN). Besides, Hierarchical Relation Graph Network (HRGN) is adopted to perform relational reasoning and predict the likelihood of linkages, effectively splitting the close text instances and grouping node classification results into arbitrary-shaped text region. We introduce a novel dataset with stroke-level annotations, namely SynthStroke, for offline pre-training of our model. Experiments on wide-ranging benchmarks verify the State-of-the-Art performance of our method. Our dataset and code will be available.