# Chargrid-OCR: End-to-end Trainable Optical Character Recognition for Printed Documents using Instance Segmentation Source: [Chargrid-OCR: End-to-end Trainable Optical Character Recognition for Printed Documents using Instance Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:06:19.934Z Authors: Christian Reisswig, Anoop R Katti, Marco Spinaci, Johannes H\\"ohne Published: 2019-09-10T13:30:55.000Z ## Abstract We present an end-to-end trainable approach for Optical Character Recognition (OCR) on printed documents. Specifically, we propose a model that predicts a) a two-dimensional character grid (\\emph{chargrid}) representation of a document image as a semantic segmentation task and b) character boxes for delineating character instances as an object detection task. For training the model, we build two large-scale datasets without resorting to any manual annotation - synthetic documents with clean labels and real documents with noisy labels. We demonstrate experimentally that our method, trained on the combination of these datasets, (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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()