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

Analysis

Why This Paper Matters

Scene text recognition is a core problem in computer vision with numerous applications, from document digitization to autonomous navigation. Prior to this work, most systems relied on multi-stage pipelines that separately handled feature extraction, character segmentation, and recognition, leading to error accumulation and complex tuning. This paper introduced CRNN (Convolutional Recurrent Neural Network), a unified architecture that fundamentally simplified the pipeline by integrating these stages into a single end-to-end trainable model. Its significance lies in demonstrating that deep learning can effectively learn to recognize sequences directly from images without explicit segmentation, a paradigm shift that influenced a wide range of sequence recognition tasks.

Technical Contributions

• End-to-end trainable architecture: Integrates CNN, RNN, and CTC loss into a single network, eliminating the need for separately trained components.
• Segmentation-free recognition: Handles sequences of arbitrary length without character-level segmentation or horizontal scale normalization, making it robust to varying text lengths and distortions.
• Lexicon-free and lexicon-based operation: The model can operate without a predefined dictionary, yet also supports lexicon-based recognition when available, offering flexibility.
• Compact model size: Achieves competitive performance with a significantly smaller model compared to prior systems, enhancing practicality for deployment.
• Generality: Successfully applied to music score recognition, showcasing the architecture's versatility beyond scene text.

Results

The paper reports experiments on standard benchmarks: IIIT-5K, Street View Text (SVT), and ICDAR datasets. In lexicon-free settings, CRNN outperformed existing methods, and in lexicon-based settings, it achieved state-of-the-art accuracy. Notably, the model size is much smaller than competing deep learning models, making it efficient for real-world use. The abstract highlights "remarkable performances" but does not provide specific numbers; however, the paper's empirical validation established CRNN as a strong baseline for years to come.

Significance

CRNN's impact extends beyond scene text recognition. Its end-to-end design and CTC-based transcription have inspired numerous follow-up works in text spotting, handwritten recognition, and other sequence labeling tasks. The architecture demonstrated that recurrent layers can effectively model sequential dependencies in visual data, paving the way for later attention-based and transformer-based models. With over 3000 citations, this paper is a cornerstone in the application of deep learning to structured prediction problems in computer vision, bridging the gap between convolutional features and sequence learning.