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We present an end-to-end trainable approach for Optical Character Recognition(OCR) on printed documents. Specifically, we propose a model that predicts a) atwo-dimensional character grid (\emph{chargrid}) representation of a documentimage as a semantic segmentation task and b) character boxes for delineatingcharacter instances as an object detection task. For training the model, webuild two large-scale datasets without resorting to any manual annotation -synthetic documents with clean labels and real documents with noisy labels. Wedemonstrate experimentally that our method, trained on the combination of thesedatasets, (i) outperforms previous state-of-the-art approaches in accuracy (ii)is easily parallelizable on GPU and is, therefore, significantly faster and(iii) is easy to train and adapt to a new domain.

Chargrid-OCR: End-to-end Trainable Optical Character Recognition for Printed Documents using Instance Segmentation