Rejecting Character Recognition Errors Using CNN Based Confidence Estimation

LI Pengchao; PENG Liangrui; WEN Juan

doi:10.1049/cje.2016.05.018

Volume 25 Issue 3

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LI Pengchao, PENG Liangrui, WEN Juan, “Rejecting Character Recognition Errors Using CNN Based Confidence Estimation,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 520-526, 2016, doi: 10.1049/cje.2016.05.018

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LI Pengchao, PENG Liangrui, WEN Juan, “Rejecting Character Recognition Errors Using CNN Based Confidence Estimation,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 520-526, 2016, doi: 10.1049/cje.2016.05.018

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Rejecting Character Recognition Errors Using CNN Based Confidence Estimation

doi: 10.1049/cje.2016.05.018

1.
Tsinghua National Laboratory for Information Science and Technology, Dept. of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2.
Equipment Academy, Beijing 101416, China

Funds: This work is supported by the National Basic Research Program of China (973 Program) (No.2014CB340506), National Natural Science Foundation of China (No.61261130590, No.61032008), and Tsinghua National Laboratory for Information Science and Technology (TNList) Cross-discipline Foundation.

Received Date: 2015-02-13
Rev Recd Date: 2015-05-08
Publish Date: 2016-05-10

Abstract

Abstract

Although Optical character recognition (OCR) technology has achieved huge progress in recent years, character misrecognition is inevitable. In order to realize high fidelity content of document digitalization, we propose a new Convolutional neural networks (CNN) based confidence estimationmethod.We detect the misrecognized characters through comparing the confidence value with a preset threshold, so as to leave the recognition errors as embedded images in the output digital documents. We adopted sofmax as the estimation of posteriori probability, overlap pooling and maxout with dropout technologies in CNN architecture design. Experimental results show that our method has achieved an explicit improvement compared to baseline system.
- Optical character recognition (OCR),
- Confidence estimation,
- Convolutional neural networks (CNN)

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