Writer identification based on handwriting

This paper describes a text-independent writer identification method. The difficulties with writer identification are discussed. These include the sensitivity of the identification algorithm to variations in the size of the training samples, in the words, line and character spacing, point sizes, and scanner resolutions. The work described demonstrates that texture analysis is a useful tool for writer identification based on handwriting. We use multichannel spatial filtering techniques to extract texture features from a nonuniformly skewed and nonskewed handwriting image. There are many available tilters in the multichannel technique. We use Gabor filters, since they have proven to be successful in extracting features for similar applications. We also use grey-scale co-occurrence matrices (GSCM) for feature extraction (for comparison purposes). Two classification techniques are adopted here, namely the weighted Euclidean distance (WED) and the k-NN classifiers. Our algorithm achieves a classification accuracy of 95.3% using 300 test images from 20 writers.