An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text...

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Scene text recognition is a challenging computer vision task crucial for image-based sequence recognition.
• Existing methods often involve separate component training and are limited by predefined lexicons or fixed sequence lengths.

Purpose of the Study:

• To propose a novel, unified neural network architecture for end-to-end scene text recognition.
• To address limitations of existing methods, including arbitrary sequence lengths and lexicon confinement.
• To develop a more practical and efficient model for real-world applications.

Main Methods:

• Developed a unified neural network integrating feature extraction, sequence modeling, and transcription.
• Implemented an end-to-end trainable architecture.
• Evaluated the model on standard benchmarks (IIIT-5K, Street View Text, ICDAR) and for music score recognition.

Main Results:

• The proposed architecture achieves superior performance over prior art on standard scene text recognition benchmarks.
• The model demonstrates effectiveness in both lexicon-free and lexicon-based recognition tasks.
• The approach shows strong generalization capabilities, performing well on image-based music score recognition.

Conclusions:

• The novel unified framework offers a more effective and efficient solution for scene text recognition.
• The end-to-end, lexicon-agnostic approach with arbitrary length handling represents a significant advancement.
• The model's practicality and generalizability are validated through extensive experiments.