Word Properties Predicting Children’s Word Recognition

Holistic Farsi handwritten word recognition using gradient features

In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...

Mixture of Experts for Persian handwritten word recognition

This paper presents the results of Persian handwritten word recognition based on Mixture of Experts technique. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed model, we used Mixture of Experts Multi Layered Perceptrons with Momentum term, in the classification ...

Predicting Word Association Strengths

This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (BoydGraber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r = 0.357 and ρ = 0.379, r = 0.344 and ρ = 0.300, an ρ = 0.292 and ρ = 0.363, respectively. We find Wor...

Predicting Human Word Associations

holistic farsi handwritten word recognition using gradient features

in this paper we address the issue of recognizing farsi handwritten words. two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. these are directional and intensity gradient features. the feature vector extracted from each stripe is then coded using the self organizing map (som). in this method each word is modeled using the discrete hidde...