COMMENTS ON OTHERS:
The authors states that the proposed study utilizes both characteristics of sketch strokes and also information provided by the gaps between the strokes, and temporal characteristics of the stroke sequence. The proposed approach is based on the use of discriminative machine learning techniques to infer the class of the stroke based on the observed ink.
The proposed model starts with considering the isolated strokes and extracting features then training probabilistic classifier based on feed forward neural network. After that, the model is augmented with temporal information to capture the correlation between class labels. Finally it is proposed that to consider information extracted from the gaps between successive strokes. The model said to be based on 9 features extracted at the independent stroke model.
The proposed work uses a HMM model to enhance the performance by taking the context for each stroke into account. It is stated that the focus is on the use of temporal context since it leads to a 1D inference problem which can solve efficiently using dynamic programming techniques. The model uses Viterbi algorithm to find the states with the training data to get the most probably selection of the strokes. It is also obvious that the HMM require initial probabilistic information for the HMM model, and it is not clear from the context that how the initial probably set is chosen to train the system. Whether authors used a dummy set of probabilities or heuristic probably was used not really clear.