SFX by Ex Libris Inc.

Title:	A novel prototype generation technique for handwriting digit recognition
Source:	Pattern Recognition [0031-3203] Impedovo, S yr:2014

Basic

Sorry, no full text available...

Please use the document delivery service (see below)

Holding information

Holdings in library search engine ALBERT

Document delivery

Request document via Library/Bibliothek

Users interested in this article also expressed an interest in the following:
description	1.	Reboucas-Filho, Gustavo P. "Recognition of handwritten digits using the signature features and Optimum-Path Forest Classifier." Revista IEEE América Latina. 14.5: 2455-2460.
description	2.	Gimenez, A. "Handwriting word recognition using windowed Bernoulli HMMs." Pattern recognition letters 35 (2014): 149-156.
description	3.	Liwicki M ., M. "Bridging the gap between handwriting recognition and knowledge management." Pattern recognition letters 35.1 (2013): 204-213.
description	4.	"Signature Identification via Efficient Feature Selection and GPU-based SVM Classifier." 2012 International Joint Conference on Neural Networks 1138-1145.
description	5.	Cungang Wang. "Handwriting Digital Recognition via Modified Logistic Regression." Journal of Multimedia 9.8 (2014): 1048-.
description	6.	Mohebi Ehsan, M. "A convolutional recursive modified Self Organizing Map for handwritten digits recognition." Neural networks 60C (2014): 104-118.
description	7.	Zamora Martínez, F. "Neural network language models for off-line handwriting recognition." Pattern recognition 47.4 (2014): 1642-1652.
description	8.	Kushki, A. "Handwriting difficulties in children with autism spectrum disorders: a scoping review.(ORIGINAL PAPER)." Journal of autism and developmental disorders 41.12 (2011): 1706-.
description	9.	Danna, J. "Signal-to-Noise velocity peaks difference: A new method for evaluating the handwriting movement fluency in children with dysgraphia." Research in developmental disabilities 34.12 (2013): 4375-4384.
description	10.	Sridhar M .. "Frontiers in handwriting processing." Pattern recognition letters 35.1 (2013): 1-2.
description	11.	Di Pietro, M. "Peripheral dysgraphia characterized by the co-occurrence of case substitutions in uppercase and letter substitutions in lowercase writing." Cortex 47.9 (2010): 1038-1051.
description	12.	"The Recognition of Handwritten Digits Based on BP Neural Network and the Implementation on Android." Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on 1498-1501.
description	13.	Xiao, X. "Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition." Pattern recognition 72 (2017): 72-81.
description	14.	Cecotti, H. "Rejection strategy for convolutional neural network by adaptive topology applied to handwritten digits recognition." 2005. 765-769.
description	15.	Voutilainen, A. "A new look at patient satisfaction: learning from self-organizing maps." Nursing research 63.5 (2014): 333-45.
description	16.	Arisoy, E. "Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition." IEEE/ACM transactions on audio, speech, and language processing 22.1 (2014): 184-192.
description	17.	Impedovo, S. "More than twenty years of advancements on Frontiers in handwriting recognition." Pattern recognition 47.3 (2013): 916-928.
description	18.	Leng, Z. "A study of the features of Internet language." 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet) 2012. 97-100.
description	19.	Frost, R. "Towards a universal model of reading." Behavioral and brain sciences 35.5 (2012): 1-17.
description	20.	Denton, Peggy L. "The effects of sensorimotor-based intervention versus therapeutic practice on improving handwriting performance in 6- to 11-year-old children.(Report)." The American Journal of Occupational Therapy 60.1 (2006): 16-.