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Using TFLearn, built a Convolutional Neural Network that takes arbitrary PNG images and translates them into LaTeX attacking problems in both computer vision and natural language processing
gigi-hsueh/img2latex
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Structure ========= - im2latex_[...].lst files - Each line is separate generated image per formula - Line structure: "[formula_idx] [image_name] [render_type]" - formula_idx is the line number of the formula in im2latex_formulas.lst - image_name is the name of rendered image (without '.png') - render_type is the name of used rendering settings (in image2latex.py) - Dataset is split to train, validation and test - train is for training the model - validation is for things testing accuracy while training / checking overfitting - test set is for final evaluation - im2latex_formulas.lst - Each line is a separate formula (formulas were stripped from "\n") - formula_images.tar.gz - tar file of the rendered images - Each image is an image of full "A4" page, where _only_ the formula is rendered - -> Lots of empty space
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Using TFLearn, built a Convolutional Neural Network that takes arbitrary PNG images and translates them into LaTeX attacking problems in both computer vision and natural language processing
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