Multiple Classifier Methods For Offline Handwritten Text Line Recognition

This paper investigates the use of multiple classifier methods for offline handwritten text line recognition. To obtain ensembles of recognisers we implement a random feature subspace method. The word sequences returned by the individual ensemble members are first aligned. Then the final word sequence is produced. For this purpose we use a voting method and two novel statistical combination methods. The conducted experiments show that the proposed multiple classifier methods have the potential to improve the recognition accuracy of single recognisers.