The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximation theory and feedforward networks
Neural Networks
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Elements of information theory
Elements of information theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
GTM: the generative topographic mapping
Neural Computation
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Document page segmentation using neuro-fuzzy approach
Applied Soft Computing
Segment confidence-based binary segmentation (SCBS) for cursive handwritten words
Expert Systems with Applications: An International Journal
Segmentation of connected handwritten digits using Self-Organizing Maps
Expert Systems with Applications: An International Journal
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Recent work on extracting features of gaps in handwritten text allows a classification of these gaps into inter-word and intra-word classes using suitable classification techniques. In this paper, we first analyse the features of the gaps using mutual information. We then investigate the underlying data distribution by using visualisation methods. These suggest that a complicated structure exists, which makes them difficult to be separated into two distinct classes. We apply five different supervised classification algorithms from the machine learning field on both the original dataset and a dataset with the best features selected using mutual information. Moreover, we improve the classification result with the aid of a set of feature variables of strokes preceding and following each gap. The classifiers are compared by employing McNemar's test. We find that SVMs and MLPs outperform the other classifiers and that preprocessing to select features works well. The best classification result attained suggests that the technique we employ is particularly suitable for digital ink manipulation at the level of words.