S3INet: Semantic-Information Space Sharing Interaction Network for Arbitrary Shape Text Detection.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Detecting arbitrary shape text in complex scenes is challenging due to variations in text appearance and background.
• Traditional methods struggle with multiscale feature fusion, information transfer, and receptive field expansion for long text.

Purpose of the Study:

• To develop an advanced arbitrarily shaped scene text detector.
• To enhance feature extraction capabilities for improved text detection accuracy.

Main Methods:

• Introduced the semantic-information space sharing interaction network (S3INet).
• Utilized the semantic-information space sharing module (S3M) for single-level multiscale feature map generation.
• Employed the multibranch parallel asymmetric convolutional module (MPACM) group to enhance text feature representation.

Main Results:

• S3INet demonstrated superior performance on natural scene and traffic text datasets.
• The method achieved significant improvements in both accuracy and robustness compared to state-of-the-art approaches.
• Evaluated on CTW-1500, Total-Text, MSRA-TD500, ICDAR2015, ICDAR2017-MLT, CTST-1600, and TPD datasets.

Conclusions:

• S3INet effectively addresses challenges in arbitrary shape text detection.
• The proposed network architecture enhances feature extraction for robust text recognition.
• The method offers a significant advancement in scene text detection technology.