An HMM Based Two-Pass Approach for Off-Line Cursive Handwriting Recognition
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This paper deals with the performance evaluation of a novel hybrid approach to large vocabulary cursive handwriting recognition and contains the following 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 on-line handwriting recognition and is now investigated for the first time for off-line 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 on-line handwritten data which has been converted to off-line data. Therefore, a multiple evaluation has been carried out, incorporating the comparison of different modeling techniques and the additional comparison of each technique for on-line and off-line recognition, using a unique database. The results confirm the fact that on-line 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 off-line recognition that are close to the results obtained for on-line recognition. Furthermore, it can be shown that for both, on-line and off-line recognition, the new hybrid approach clearly outperforms the competing traditional HMM techniques. It is also demonstrated that the new hybrid approach yields superior results for the off-line recognition of machine-printed multifont characters.