|Table of Contents|

 DBN Fusion Model for Offline Handwritten
Chinese Characters Recognition
(PDF)

《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

Issue:
2017年06期
Page:
82-86
Research Field:
材料科学与工程
Publishing date:

Info

Title:
 DBN Fusion Model for Offline Handwritten
Chinese Characters Recognition
Author(s):
 LIU Lu SUN Wei-wei DING Bo
 ( School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China)
Keywords:
 offline handwritten character quadratic discriminant function deep belief network Chinese
character recognition
PACS:
TP391. 412
DOI:
15938 /j. jhust. 2017. 06. 016
Abstract:
 The requirement of the recognition result is also increasing in practical applications. In this paper,a
new classifier cascade recognition model is proposed for the problem of offline handwritten Chinese character
recognition. New model is the fusion of modified quadratic discriminant function ( MQDF) and deep belief network
( DBN) . First to recognize and get result using MQDF,and calculate the reliability of the recognition result. If the
reliability can meet the requirement,MQDF recognition result can be as the final result directly output. Otherwise
using the DBN to make recognition again and getting the final recognition result. Experiments show that the MQDF
and DBN fusion model proposed in this paper can achieve better accuracy than the single use of MQDF and DBN in
the offline handwritten Chinese character recognition task,which is performed on the ETL-9B handwritten Chinese
character dataset.

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Last Update: 2018-03-10