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» Using Random Forests for Handwritten Digit Recognition
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ESANN
2006
13 years 6 months ago
Recognition of handwritten digits using sparse codes generated by local feature extraction methods
We investigate when sparse coding of sensory inputs can improve performance in a classification task. For this purpose, we use a standard data set, the MNIST database of handwritte...
Rebecca Steinert, Martin Rehn, Anders Lansner
ICPR
2002
IEEE
14 years 5 months ago
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
This paper discusses the use of genetic algorithm for feature selection for handwriting recognition. Its novelty lies in the use of a multi-objective genetic algorithms where sens...
Luiz E. Soares de Oliveira, Robert Sabourin, Fl&aa...
ICDAR
2009
IEEE
13 years 2 months ago
Online Handwritten Japanese Character String Recognition Using Conditional Random Fields
This paper describes an online handwritten Japanese character string recognition system based on conditional random fields, which integrates the information of character recogniti...
Xiang-Dong Zhou, Cheng-Lin Liu, Masaki Nakagawa
ICDAR
2007
IEEE
13 years 11 months ago
Handwritten Word Recognition Using Conditional Random Fields
The paper describes a lexicon driven approach for word recognition on handwritten documents using Conditional Random Fields(CRFs). CRFs are discriminative models and do not make a...
Shravya Shetty, Harish Srinivasan, Sargur N. Sriha...