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AAF-Net: Scene text detection based on attention aggregation features.

Mengmeng Chen^1,2, Mayire Ibrayim^1,2, Askar Hamdulla^1,2

¹College of Information Science and Engineering, Xinjiang University, Urumqi, China.

|August 5, 2022

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This study introduces a novel Cross-Scale Attention Aggregation Feature Pyramid Network (CSAA-FPN) for improved scene text detection. The CSAA-FPN enhances feature representation, accurately detecting small and adjacent text instances, outperforming existing methods.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Scene text detection faces challenges with small, multi-directional, and adjacent text instances due to neural network receptive field limitations.
Existing methods often struggle with low detection rates and false positives for complex text arrangements.

Purpose of the Study:

To propose a new feature pyramid network, the Cross-Scale Attention Aggregation Feature Pyramid Network (CSAA-FPN), for enhanced scene text detection.
To address limitations in detecting small, arbitrarily oriented, and closely packed text instances.

Main Methods:

Developed a novel Cross-Scale Attention Aggregation Feature Pyramid Network (CSAA-FPN).
Incorporated an Attention Aggregation Feature Module (AAFM) to enhance features and handle multi-scale information.

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Utilized CBAM attention module for focused feature extraction and an Adaptive Fusion Module (AFM) for feature refinement.

Main Results:

The proposed CSAA-FPN effectively enhances features, improving the detection of small and adjacent text instances.
Experiments on CTW1500, Total-Text, ICDAR2015, and MSRA-TD500 datasets demonstrate the model's superior performance.

Conclusions:

The CSAA-FPN model offers a significant advancement in scene text detection accuracy.
The integration of attention mechanisms and adaptive fusion effectively tackles challenges posed by complex text scenes.