Papers › Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
Christoph Wick, Christian Reul, Frank Puppe
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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