Teaching Machines to Code: Neural Markup Generation with Visual Attention

15 Feb 2018  ·  Sumeet S. Singh ·

We present a neural transducer model with visual attention that learns to generate LaTeX markup of a real-world math formula given its image. Applying sequence modeling and transduction techniques that have been very successful across modalities such as natural language, image, handwriting, speech and audio; we construct an image-to-markup model that learns to produce syntactically and semantically correct LaTeX markup code over 150 words long and achieves a BLEU score of 89%; improving upon the previous state-of-art for the Im2Latex problem. We also demonstrate with heat-map visualization how attention helps in interpreting the model and can pinpoint (detect and localize) symbols on the image accurately despite having been trained without any bounding box data.

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Introduced in the Paper:

I2L-140K Im2latex-90k

Used in the Paper:

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Optical Character Recognition I2L-140K I2L-NOPOOL BLEU 89.09% # 1
Optical Character Recognition I2L-140K I2L-STRIPS BLEU 89% # 2
Optical Character Recognition im2latex-100k I2L-STRIPS BLEU 88.86% # 1


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