Handwritten Nastaleeq Script Recognition with BLSTM-CTC and ANFIS method

A recurrent neural network (RNN) has been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nastaleeq scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. The propose method use multidimensional BLSTM and ANFIS Method for OCR recognition. The ANFIS approach learns the rules and membership functions from data. ANFIS is an adaptive network. An adaptive network is network of nodes and directional links. These networks are learning a relationship between inputs and outputs. The Recognition error rate is 5.4 %. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shapematching based method is also presented.