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Unsupervised OCR Model Evaluation Using GAN

Abhash Sinha; Martin Jenckel; Syed Saqib Bukhari; Andreas Dengel
In: Proceedings ICDAR'19. International Conference on Document Analysis and Recognition (ICDAR-2019), September 20-25, Sydney, Australia, Pages 1256-1261, ISBN 978-1-7281-3015-6, IEEE, 9/2019.


Optical Character Recognition (OCR) has achieved its state-of-the-art performance with the use of Deep Learning for character recognition. Deep Learning techniques need large amount of data along with ground truth. Out of the available data, small portion of it has to be used for validation purpose as well. Preparing ground truth for historical documents is expensive and hence availability of data is of utmost concern. Jenckel et al. jenckel came up with an idea of using all the available data for training the OCR model and for the purpose of validation, they generated the input image from Softmax layer of the OCR model; using the decoder setup which can be used to compare with the original input image to validate the OCR model. In this paper, we have explored the possibilities of using Generative Adversial Networks (GANs) [6] for generating the image directly from the text obtained from OCR model instead of using the Softmax layer which is not always accessible for all the Deep Learning based OCR models. Using text directly to generate the input image back gives us the advantage to use this pipeline for any OCR models even whose Softmax layer is not accessible. In the results section, we have shown that the current state of using GANs for unsupervised OCR model evaluation.

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