Image-to-Markup Generation with Coarse-to-Fine Attention (1609.04938v2)

Abstract: We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism. Our method is evaluated in the context of image-to-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTC-based models, attention-based approaches can tackle this non-standard OCR task. Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data. To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention.

• The paper introduces a novel encoder-decoder architecture with a coarse-to-fine attention mechanism for converting math expression images into LaTeX markup.
• It incorporates a multi-level attention system with a row encoder to efficiently localize characters in complex mathematical layouts.
• Benchmark tests on the Im2Latex-100k dataset demonstrate over 75% exact match accuracy, outperforming traditional OCR systems like InftyReader.

Image-to-Markup Generation with Coarse-to-Fine Attention

The paper in question presents a novel approach to the problem of converting images containing mathematical expressions into LaTeX markup using a neural encoder-decoder model with a coarse-to-fine attention mechanism. The research tackles a specific domain within Optical Character Recognition (OCR) that translates visual data into structured language or markup, focusing explicitly on mathematical equations where the layout matters significantly.

Key Contributions

1. Neural Model Design: This research introduces an encoder-decoder architecture that leverages a multi-level attention-based approach. One distinctive feature is integrating a row encoder into a convolutional feature extraction network, enhancing the model's ability to localize and interpret characters within the context of mathematical expressions.
2. Coarse-to-Fine Attention Mechanism: The paper's central innovation is the coarse-to-fine attention mechanism. This strategy selects a support region in a coarse, computationally inexpensive manner before applying fine-grained attention. By doing so, it addresses the comprehensive computational complexity typical of soft attention mechanisms, particularly beneficial when dealing with large image sizes and complex equations.
3. Dataset Introduction: The authors also provide a new dataset called Im2Latex-100k. It consists of over 100,000 rendered mathematical expressions with corresponding LaTeX markup drawn from real-world sources, providing a practical testbed for evaluating image-to-markup algorithms.
4. Benchmark Results: The model significantly outperforms classical OCR systems and other neural baseline systems on this newly introduced benchmark. Classical systems like InftyReader, which previously dominated the scene, fall short in comparison, especially when considering the generated output's exactness and presentation fidelity.

Experimental Findings

The paper details several experiments, quantifying the advantages of the proposed model and examining various ablations and alternative configurations. The Im2Tex model demonstrated its strength by achieving over 75% exact match accuracy for rendering the generated LaTeX to match the source images. Additionally, despite inherent ambiguities in LaTeX markup, the proposed normalization techniques helped in achieving a high BLEU score without artificially boosting results, legitimizing the model's capability of inferring correct structures and semantics.

Implications and Future Directions

Practically, this research paves the way for sophisticated, fully neural approaches in domains where understanding and generating structured text is crucial, such as educational technology, digital archiving of scientific documents, and automated typesetting systems. The introduction of the coarse-to-fine attention mechanism could influence the design of future neural networks by highlighting more efficient methodologies for handling large input spaces in image-to-text translation tasks.

Theoretically, this model challenges the assumption that strict order constraints, like those in CTC models, are necessary for producing structured markup languages. It demonstrates that a neural approach can inherently learn the complex ordering and spatial dependencies required for accurate semantic translation.

Moving forward, extending this framework to other structured languages beyond mathematical markup, such as complex diagrams or annotated images, could further demonstrate the versatility and robustness of this approach. Furthermore, integrating memory networks or other advanced neural structures could enhance the model's capacity, allowing handling even more intricate layouts and formats.

Overall, while the elements explored in this paper signify substantial advancement, the methodologies introduced here present a foundation for evolving neural OCR and image-to-text models toward greater efficiency and accuracy.