Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
Abstract
This paper proposes a combination of a convolutional and a LSTM network to improve the accuracy of OCR on early printed books. While the standard model of line based OCR uses a single LSTM layer, we utilize a CNN- and Pooling-Layer combination in advance of an LSTM layer. Due to the higher amount of trainable parameters the performance of the network relies on a high amount of training examples to unleash its power. Hereby, the error is reduced by a factor of up to 44%, yielding a CER of 1% and below. To further improve the results we use a voting mechanism to achieve character error rates (CER) below . The runtime of the deep model for training and prediction of a book behaves very similar to a shallow network.
Cite
@article{arxiv.1802.10033,
title = {Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks},
author = {Christoph Wick and Christian Reul and Frank Puppe},
journal= {arXiv preprint arXiv:1802.10033},
year = {2018}
}
Comments
16 pages, 4 figures, 8 tables, submitted to JLCL Volume 33 (2018), Issue 1