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The recognition of handwritten mathematical expressions in images and videoframes is a difficult and unsolved problem yet. Deep convectional neuralnetworks are basically a promising approach, but typically require a largeamount of labeled training data. However, such a large training dataset doesnot exist for the task of handwritten formula recognition. In this paper, weintroduce a system that creates a large set of synthesized training examples ofmathematical expressions which are derived from LaTeX documents. For thispurpose, we propose a novel attention-based generative adversarial network totranslate rendered equations to handwritten formulas. The datasets generated bythis approach contain hundreds of thousands of formulas, making it ideal forpretraining or the design of more complex models. We evaluate our synthesizeddataset and the recognition approach on the CROHME 2014 benchmark dataset.Experimental results demonstrate the feasibility of the approach.

Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention