Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention
| Autor: | Eric Müller-Budack, Matthias Springstein, Ralph Ewerth |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Training set Ideal (set theory) Artificial neural network Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine learning computer.software_genre Machine Learning (cs.LG) Task (project management) Adversarial system ComputingMethodologies_PATTERNRECOGNITION Benchmark (computing) Artificial intelligence business computer Generative adversarial network Generative grammar |
| Zdroj: | MMPT@ICMR |
| DOI: | 10.1145/3463945.3469059 |
| Popis: | The recognition of handwritten mathematical expressions in images and video frames is a difficult and unsolved problem yet. Deep convectional neural networks are basically a promising approach, but typically require a large amount of labeled training data. However, such a large training dataset does not exist for the task of handwritten formula recognition. In this paper, we introduce a system that creates a large set of synthesized training examples of mathematical expressions which are derived from LaTeX documents. For this purpose, we propose a novel attention-based generative adversarial network to translate rendered equations to handwritten formulas. The datasets generated by this approach contain hundreds of thousands of formulas, making it ideal for pretraining or the design of more complex models. We evaluate our synthesized dataset and the recognition approach on the CROHME 2014 benchmark dataset. Experimental results demonstrate the feasibility of the approach. Accepted for publication in: ACM International Conference on Multimedia Retrieval (ICMR) Workshop 2021 |
| Databáze: | OpenAIRE |
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