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ICPR
2006
IEEE
16 years 7 months ago
Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition
In this paper we propose a novel scheme to combine offline and online features of handwritten strokes. The stateof-the-art methods in handwritten stroke recognition have used a pr...
C. V. Jawahar, Karteek Alahari, Satya Lahari Putre...
ICDAR
2009
IEEE
15 years 4 months ago
Combining Multiple HMMs Using On-line and Off-line Features for Off-line Arabic Handwriting Recognition
This paper presents an off-line Arabic Handwriting recognition system based on the selection of different state of the art features and the combination of multiple Hidden Markov M...
Mahdi Hamdani, Haikal El Abed, Monji Kherallah, Ad...
ECIR
2011
Springer
14 years 10 months ago
Fractional Similarity: Cross-Lingual Feature Selection for Search
Abstract. Training data as well as supplementary data such as usagebased click behavior may abound in one search market (i.e., a particular region, domain, or language) and be much...
Jagadeesh Jagarlamudi, Paul N. Bennett
ICIP
2007
IEEE
16 years 8 months ago
MuFeSaC: Learning When to Use Which Feature Detector
Interest point detectors are the starting point in image analysis for depth estimation using epipolar geometry and camera ego-motion estimation. With several detectors defined in ...
Sreenivas R. Sukumar, David L. Page, Hamparsum Boz...
ICASSP
2008
IEEE
16 years 28 days ago
Frame-based acoustic feature integration for speech understanding
With the purpose of improving Spoken Language Understanding (SLU) performance, a combination of different acoustic speech recognition (ASR) systems is proposed. State a-posteriori...
Loic Barrault, Christophe Servan, Driss Matrouf, G...