Handwriting recognition using local methods for normalization and global methods for recognition

A major problem in handwriting recognition is the huge variability and distortions of patterns. Elastic models based on local observations and dynamic programming such HMM are efficient to absorb this variability, but their vision is local. Furthermore, global models such neural network having a fixed input size are efficient to make correlations on an entire pattern, but they cannot deal with length variability and are very sensitive to distortions. This paper proposes to use the power of these two classes of models. The elastic model is used to normalize the input-image and the fixed model performs the recognition.. The elastic model-uses an NSHP-HMM and the global model uses a support vector machine (SVM). The NSHP-HMM searches the important features and absorbs the distortions. According to the localisation of these features a pattern can be normalized to a standard size. Then the SVM is used to estimate global correlations and classify the pattern. The first results obtained are encouraging and confirm the validity of our approach.