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arXiv · 1802.10033

Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks

Christoph Wick·Christian Reul·Frank Puppe
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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 $0.5%$. The runtime of the deep model for training and prediction of a book behaves very similar to a shallow network.

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Christoph Wick, Christian Reul, Frank Puppe. 2018-02-27. Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks. https://arxiv.org/abs/1802.10033

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