Improving Mdlstm For Offline Arabic Handwriting Recognition Using Dropout At Different Positions

RNN and LSTM are now a state-of-the-art technology that provide a very good performance on different machine learning tasks as handwritten Arabic word recognition. This field remains an on-going research problem due to its cursive appearance, the variety of writers and the diversity of styles. In this work, we propose a new offline Arabic handwriting recognition system based on a particular RNN named the MDLSTM on which we propose to apply dropout technique in different positions such as before, after or inside the MDLSTM layers. This regularization technique has the advantages of preventing our system against overfitting problem and reducing the error recognition rate. We carried out experiments on the well-known IFN/ENIT Database.