Paper Info
Title
A Comprehensive Analysis of Misclassified Handwritten Chinese Character Samples by Incorporating Human Recognition
Abstract
The development of convolutional neural networks (CNN) has led to revolutionary progress in the resolution of the offline handwritten Chinese character recognition (HCCR) problem. As the recognition rate on a standard offline HCCR testbed is outstanding, a few samples that remain misclassified have kindled our interest. In this paper, with the help of human recognition results, we present a comprehensive analysis of the samples misclassified by a state-of-the-art CNN model. We performed the analysis based on the top-1-votes, which are obtained from the statistical analysis of human recognition results, and derived the following conclusions: (1) the majority of samples with high top-1-votes were mis-labeled. Besides, by comparing the results of human recognition with that of CNN, some limitations of CNN that provide scope for further improvement are presented; (2) in the samples with medium top- 1-votes, it is shown that the samples with different confidence level have different characteristics. Specifically, some samples could be regarded as multi-label samples; (3) the samples with low top-1- votes are either wrongly written or written extensively in cursive style, which are difficult to match their given ground-truths; (4)the relationship between writing styles and misclassifications are also introduced in the paper. We believe this work should provide some insights and brings new clues on designing new classification methods to deal with these challenging samples.
Year
DOI
Venue
2017
10.1109/ICDAR.2017.82
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Keywords
Field
DocType
Convolutional neural network,offline handwritten Chinese character recognition,misclassified samples,human recognition result
Cursive,Pattern recognition,Character recognition,Convolutional neural network,Computer science,Writing style,Handwriting recognition,Testbed,Artificial intelligence,Statistical analysis
Conference
Volume
ISSN
ISBN
01
1520-5363
978-1-5386-3587-2
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Kaihuan Liang110.69
Lianwen Jin21337113.14
zecheng xie3967.55
XueFeng Xiao4221.43
Weiguo Huang542.15