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A Further Step to Perfect Accuracy by Training CNN with Larger Data | IEEE Conference Publication | IEEE Xplore

A Further Step to Perfect Accuracy by Training CNN with Larger Data


Abstract:

Convolutional Neural Networks (CNN) are on the forefront of accurate character recognition. This paper explores CNNs at their maximum capacity by implementing the use of ...Show More

Abstract:

Convolutional Neural Networks (CNN) are on the forefront of accurate character recognition. This paper explores CNNs at their maximum capacity by implementing the use of large datasets. We show a near-perfect performance by using a dataset of about 820,000 real samples of isolated handwritten digits, much larger than the conventional MNIST database. In addition, we report a near-perfect performance on the recognition of machine-printed digits and multi-font digital born digits. Also, in order to progress toward a universal OCR, we propose methods of combining the datasets into one classifier. This paper reveals the effects of combining the datasets prior to training and the effects of transfer learning during training. The results of the proposed methods also show an almost perfect accuracy suggesting the ability of the network to generalize all forms of text.
Date of Conference: 23-26 October 2016
Date Added to IEEE Xplore: 16 January 2017
ISBN Information:
Print ISSN: 2167-6445
Conference Location: Shenzhen, China

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