Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data

The paper deals with the performance evaluation of a novel hybrid approach to large vocabulary cursive handwriting recognition and contains various innovations. 1) It presents the investigation of a new hybrid approach to handwriting recognition, consisting of hidden Markov models (HMMs) and neural networks trained with a special information theory based training criterion. This approach has only been recently introduced successfully to online handwriting recognition and is now investigated for the first time for offline recognition. 2) The hybrid approach is extensively compared to traditional HMM modeling techniques and the superior performance of the new hybrid approach is demonstrated. 3) The data for the comparison has been obtained from a database containing online handwritten data which has been converted to offline data. Therefore, a multiple evaluation has been carried out, incorporating the comparison of different modeling techniques and the additional comparison of each technique for online and offline recognition, using a unique database. The results confirm that online recognition leads to better recognition results due to the dynamic information of the data, but also show that it is possible to obtain recognition rates for offline recognition that are close to the results obtained for online recognition. Furthermore, it can be shown that for both online and offline recognition, the new hybrid approach clearly outperforms the competing traditional HMM techniques. It is also shown that the new hybrid approach yields superior results for the offline recognition of machine printed multifont characters