Combination of global and local baseline-independent features for offline Arabic handwriting recognition

In this paper, we propose a novel method for extracting a set of baseline-independent features, which are based on the combination of global and local information. A HMM-based recognition system is developed with 161 models that include a space model and a blank model. All of the models are trained using the standard Baum-Welch Algorithm with the state-tying technique, and are then decoded using the Viterbi Algorithm. Experiments are conducted on the benchmark IFN/ENIT database. Results show that our proposed features can make good use of the relationship between adjacent characters and are sufficiently robust, especially when characters are shifted up or down and when the handwriting width varies.