Multiclass learning for writer identification using error-correcting codes

Abstract

Writer Identification can be seen as a multi-class learning problem where number of writers are different classes. One of the fundamental approaches to solve a multi-class problemis by breaking it into binary classification tasks. In this work weare proposing a generic approach for multi-class classification using an ensemble of binary classifiers. We assign a distributedoutput representation to each class in the form of codewords andan ensemble of binary classifiers is created where each classifierpredicts one bit of the codeword. Actual label is determined using Belief Propagation algorithm on a graph constructed from the code matrix. We have performed experiments on a new publiclyavailable IBM-UB-1 dataset for the task of writer identification to show the efficacy of our method. © 2014 IEEE.