Feature design for offline Arabic handwriting recognition: handcrafted vs automated?

In handwriting recognition, design of relevant feature is a very important but daunting task. On one hand, handcraft design of features is difficult, depending on expert knowledge and on heuristics. On the other hand, biologically inspired neural networks are able to learn automatically features from the input image, but requires a good underlying model. The goal of this paper is to evaluate the performance of automatically learned features compared to handcrafted features, as they provide a promising alternative to the difficult task of features handcrafting. In this work, the recognition model is based on the long short-term memory (LSTM) and connectionist temporal classification (CTC) neural networks. This model has been shown to outperform the well-known HMM model for various handwriting tasks, thanks to its reliable probabilistic modeling. In its multidimensional form, called MDLSTM, this network is able to automatically learn features from the input image. For evaluation, we compare the MDLSTM learned features and four state-of-the-art handcrafted features. The IFN/ENIT database has been used as benchmark for Arabic word recognition, where the results are promising.