Urdu Nastaliq recognition using convolutional–recursive deep learning

• Published in: Neurocomputing (Amsterdam) Vol. 243; pp. 80 - 87
• Main Authors: Naz, Saeeda, Umar, Arif I., Ahmad, Riaz, Siddiqi, Imran, Ahmed, Saad B, Razzak, Muhammad I., Shafait, Faisal
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 21-06-2017
• Subjects: BLSTM; CNN; CTC; MDLSTM; RNN; Urdu OCR; MDLSTM; CNN; RNN; Urdu OCR; CTC; BLSTM
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2017.02.081

Recent developments in recognition of cursive scripts rely on implicit feature extraction methods that provide better results as compared to traditional hand-crafted feature extraction approaches. We present a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script. The first layer extracts low-level translational invariant features using Convolutional Neural Networks (CNN) which are then forwarded to Multi-dimensional Long Short-Term Memory Neural Networks (MDLSTM) for contextual feature extraction and learning. Experiments are carried out on the publicly available Urdu Printed Text-line Image (UPTI) dataset using the proposed hierarchical combination of CNN and MDLSTM. A recognition rate of up to 98.12% for 44-classes is achieved outperforming the state-of-the-art results on the UPTI dataset.