Serial classifier combination for handwritten word recognition

The performance of off-line handwritten word recognition algorithms declines with increasing lexicon size, but may be improved by serial combination of classifiers. The authors address some issues relevant to the design of serial classifier combinations. They present experimental results that show that the performance of a serial combination depends on not only the intrinsic recognition power of the classifiers but also the relative orthogonality of their features. A top-choice recognition rate of 83% is obtained for a lexicon of size 1700 by combining two analytical word classifiers that perform individually at 70%. Even higher recognition rates may be expected from a serial combination of two classifiers with less correlated features, such as a high-performance holistic classifier with an analytical classifier.