ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting

Area of Science:

• Computer Vision
• Natural Language Processing
• Deep Learning

Background:

• Scene text spotting is crucial for computer vision applications.
• Current methods struggle to effectively integrate linguistic knowledge into deep networks.
• Limitations in language models include implicit modeling, unidirectional representation, and noisy inputs.

Purpose of the Study:

• To propose an advanced scene text spotting model, ABINet++, that overcomes limitations of existing language models.
• To enhance the accuracy and efficiency of scene text recognition and spotting through improved linguistic modeling.
• To demonstrate the model's effectiveness on diverse benchmarks and challenging image conditions.

Main Methods:

• Developed ABINet++ featuring autonomous, bidirectional, and iterative language modeling.
• Introduced a novel bidirectional cloze network (BCN) for language understanding.
• Implemented iterative correction and a self-training method for robustness and learning from unlabeled data.
• Integrated Transformer units and a position-content attention module for long text recognition.

Main Results:

• ABINet++ achieved state-of-the-art performance on scene text recognition and spotting benchmarks.
• The model shows superiority in various environments, particularly with low-quality images.
• Experiments in English and Chinese confirmed significant improvements in accuracy and speed compared to existing methods.

Conclusions:

• The proposed autonomous, bidirectional, and iterative language modeling approach significantly advances scene text spotting.
• ABINet++ offers a robust and efficient solution for text recognition and spotting tasks.
• The method demonstrates broad applicability and effectiveness across different languages and image qualities.