Efficient, LexiconFree OCR using Deep Learning Contrary to popular belief, Optical Character Recognition (OCR) remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrical distortions, complex backgrounds, and diverse fonts. In this paper, we present a segmentationfree OCR system that combines deep learning methods, synthetic training data generation, and data augmentation techniques. We render synthetic training data using large text corpora and over 2000 fonts. To simulate text occurring in complex natural scenes, we augment extracted samples with geometric distortions and with a proposed data augmentation technique  alphacompositing with background textures. Our models employ a convolutional neural network encoder to extract features from text images. Inspired by the recent progress in neural machine translation and language modeling, we examine the capabilities of both recurrent and convolutional neural networks in modeling the interactions between input elements. The proposed OCR system surpasses the accuracy of leading commercial and opensource engines on distorted text samples.

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.