All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection

TL;DR

This paper introduces NASK, a two-stage segmentation-based method for detecting arbitrarily shaped texts in images, improving accuracy by combining coarse proposals with fine geometric refinement.

Contribution

The paper proposes a novel two-stage detection framework with specialized modules for better handling complex text shapes, outperforming existing methods.

Findings

NASK achieves superior performance on Total-Text, SCUT-CTW1500, and ICDAR 2015 benchmarks.

The method effectively captures complex text geometries with high accuracy.

Extensive experiments validate the robustness and effectiveness of the proposed approach.

Abstract

Arbitrary-shaped text detection is a challenging task since curved texts in the wild are of the complex geometric layouts. Existing mainstream methods follow the instance segmentation pipeline to obtain the text regions. However, arbitraryshaped texts are difficult to be depicted through one single segmentation network because of the varying scales. In this paper, we propose a two-stage segmentation-based detector, termed as NASK (Need A Second looK), for arbitrary-shaped text detection. Compared to the traditional single-stage segmentation network, our NASK conducts the detection in a coarse-to-fine manner with the first stage segmentation spotting the rectangle text proposals and the second one retrieving compact representations. Specifically, NASK is composed of a Text Instance Segmentation (TIS) network (1st stage), a Geometry-aware Text RoI Alignment (GeoAlign) module, and a Fiducial pOint eXpression (FOX) module (2nd stage). Firstly, TIS extracts the augmented features with a novel Group Spatial and Channel Attention (GSCA) module and conducts instance segmentation to obtain rectangle proposals. Then, GeoAlign converts these rectangles into the fixed size and encodes RoI-wise feature representation. Finally, FOX disintegrates the text instance into serval pivotal geometrical attributes to refine the detection results. Extensive experimental results on three public benchmarks including Total-Text, SCUTCTW1500, and ICDAR 2015 verify that our NASK outperforms recent state-of-the-art methods.