Paper Info
Title
Recognition of Handwritten Characters in Chinese Legal Amounts by Stacked Autoencoders
Abstract
Handwritten Characters Recognition has long been a tough problem in pattern recognition and machine learning. Some special tasks, such as automatic check preprocessing, require Handwritten Chinese Legal Amounts recognition as a prerequisite. Since we expect to utilize machine instead of human to process bank checks, the recognition rate in such task must reach a relatively high rate. This paper proposes to use deep learning, auto-encoder as an effective approach for obtaining hierarchical representations of Isolated Handwritten Chinese Legal Amounts. Experiments show such representations are highly abstractive and can be used in character recognition. Meanwhile, a novel way by combining multiple Neural Networks in doing the work is proposed which proves to be able to improve the recognition rate significantly. This method reports a 0.64% error rate on a large test set over 10,000 samples and outperforms traditional methods using hand-crafted features and convolutional neural network committees (another deep learning model), narrowing the gap to human performance.
Year
DOI
Venue
2014
10.1109/ICPR.2014.518
ICPR
Keywords
Field
DocType
Chinese Legal Amount,Committee,Elastic Meshing,Isolated Character Recognition,Sparse Auto-encoder
Neocognitron,Intelligent character recognition,Pattern recognition,Convolutional neural network,Computer science,Document processing,Speech recognition,Feature (machine learning),Artificial intelligence,Deep learning,Artificial neural network,Intelligent word recognition
Conference
ISSN
Citations 
PageRank 
1051-4651
4
0.45
References 
Authors
15
3
Name
Order
Citations
PageRank
Hsin-min Wang11201129.62
Yiping Chen214820.86
Wang X.363.18