NDLI: Handwritten Brazilian month recognition: an analysis of two NN architectures and a rejection mechanism

Content Provider	IEEE Xplore Digital Library
Author	Kapp, M.N. Freitas, C.O.D.A. Sabourin, R.
Copyright Year	2004
Description	Author affiliation: Pontificia Universidade Catolica do Parana, Curitiba, Brazil (Kapp, M.N.; Freitas, C.O.D.A.)
Abstract	This paper evaluates the use of the conventional architecture feedforward MLP (multiple layer perceptron) and class-modular for the handwriting recognition (HWR) and it also compares the results obtained with previous works in terms of recognition rate. This work presents a feature set in full detail to work with HWR. The experiments showed that the class-modular architecture is better than conventional architecture. The obtained average recognition rates were 77.08% using the conventional architecture and 81.75% using the class-modular. This paper also describes a performance study in which a rejection mechanism with multiple thresholds is evaluated for both conventional and class-modular architectures. The multiple thresholds idea is based on the use of N class-related reject thresholds (CRTs). The results indicate that this rejection mechanism can be used appropriately in both architectures. The experimental results are 86.38% and 91.52% using a handwritten months word database.
Sponsorship	Hitachi, Japan IBM, USA Fujitsu Lab., Japan NEC, Japan Toshiba, Japan Microsoft, USA Int. Assoc. for Pattern Recognition
Starting Page	209
Ending Page	214
File Size	169319
Page Count	6
File Format	PDF
ISBN	0769521878
ISSN	15505235
DOI	10.1109/IWFHR.2004.53
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-26
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Neural networks Cathode ray tubes Feature extraction Databases System performance Artificial neural networks Power generation Feedforward neural networks Data mining
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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