Unsupervised OCR Model Evaluation Using GAN Optical Character Recognition (OCR) has achieved its stateoftheart 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 Preparing ground truth for historical documents is expensive and hence availability of data is of utmost concern.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) gan 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.

Data Augmentation via Adversarial Networks for Optical Character RecognitionConference Submissions With the ongoing digitalization of ressources across the industry, robust OCR solutions (Optical Character Recognition) are highly valuable. In this work, we aim at designing models to read typical damaged faxes and PDF files and training them with unlabeled data. Stateofart deep learning architectures require scalable tagged datasets that are often difficult and costly to collect. To ensure compliance standards or to provide reproducible cheap and fast solutions for training OCR systems, producing datasets that mimic the quality of the data that will be passed to the model is paramount. In this paper we discuss using unsupervised imagetoimage translation methods to learn transformations that aim to map clean images of words to damaged images of words. The quality of the transformation is evaluated through the OCR brick and these results are compared to the Inception Score (IS) of the GANs we used. That way we are able to generate an arbitrary large realistic dataset without labeling a single observation. As a result, we propose an endtoend OCR training solution to provide competitive models.