Rejecting Character Recognition Errors Using CNN Based Confidence Estimation  

作　　者：LI Pengchao PENG Liangrui WEN Juan 

机构地区：[1]Tsinghua National Laboratory for Information Science and Technology, Dept. of Electronic Engineering, Tsinghua University, Beijing 100084, China [2]Equipment Academy, Beijing 101416, China

出　　处：《Chinese Journal of Electronics》2016年第3期520-526,共7页电子学报（英文版）

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

摘　　要：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 estimation method. 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.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 estimation method. 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.

关 键 词：Optical character recognition（OCR） Confidence estimation Convolutional neural networks（CNN） 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]