Sequence Recognition with Scanning N-Tuple Ensembles

The Scanning N-Tuple classifier (SNT) is a fast and accurate method for classifying sequences. Applications include both on-line and off-line hand-written character recognition. SNTs have conventionally been trained using maximum likelihood parameter estimation. This paper describes a discriminative training rule that can be applied to ensembles of SNTs. Results demonstrate a significant improvement for the discriminative ensemble method. For comparison purposes we also implemented a Support Vector Machine (SVM) operating in the sequence domain. We tested each method on a chain-coded version of the MNIST hand-written digit dataset. The SNT is not quite as accurate as the SVM, but is much faster both in training and recognition.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Simon M. Lucas, Tzu-Kuo Huang
• Keywords: Computer Vision, Discriminative Ensemble Method, Discriminative Training Rule, Hand-written Digit Dataset, ICPR 2004