Recognition of printed Urdu script in Nastaleeq font by using CNN-BiGRU-GRU Based Encoder-Decoder Framework

• Published in: Intelligent systems with applications 2023-05, Vol.18, p.200194, Article 200194
• Main Authors: Zia, Sohail, Azhar, Muhammad, Lee, Bumshik, Tahir, Adnan, Ferzund, Javed, Murtaza, Fozia, Ali, Moazam
• Format: Article
• Language: English
• Subjects: BiGRU; Deep learning; Encoder-Decoder model; GRU; OCR; Sequence learning
• ISSN: 2667-3053; 2667-3053
• DOI: 10.1016/j.iswa.2023.200194

•This research was conducted from January, 2021 to January, 2023.•RNN based Deep learning models has shown tremendous success in sequential and temporal data where the order is critical to achieve higher accuracy in context understanding. RNN family like LSTM, BLSTM, GRU, BiGRU etc. are the mainly used models in these kind of sequential tasks.•RNN family based Encoder-decoder frameworks are widely used for the recognition of various languages scripts. However, in Urdu, very less research has been done especially with the deep learning models.•The existing proposed algorithms for printed Urdu recognition only work for very basic sentences of Urdu but in case of complex words and sentences, these algorithms totally fail in terms of accuracy and the time complexity in identification of the Nastaleeq font writing.•To identify printed Urdu text in images, we have proposed an encoder-decoder based hybrid deep learning approach with Convolutional Neural Network (CNN) for feature extraction part, bi-directional Gated Recurrent Unit network (BiGRU) as encoder and Gated Recurrent Unit network (GRU) as decoder. The CNN layer of the algorithm is used to obtain ligature features in Urdu, which are subsequently utilized by encoder (BiGRU) and decoder (GRU) to recognize the sentences by accurately distinguishing the characters and joiners.•Experimental results have shown that our proposed CNN-BiGRU-GRU hybrid technique with specific hyper-parameter tuning performs well as compared to other state-of-the-art algorithms. RNN based Deep learning models has shown tremendous success in sequential and temporal data where the order is critical to achieve higher accuracy in context understanding. RNN family like LSTM, BLSTM, GRU, BiGRU etc. are the mainly used models in these kind of sequential tasks. RNN family based Encoder-decoder frameworks are widely used for the recognition of various languages scripts. However, in Urdu, very less research has been done especially with the deep learning models. The existing research work for printed Urdu recognition have shown that the current models only work for very basic sentences of Urdu but in case of complex words and sentences, these algorithms totally fail in terms of accuracy and the time complexity in identification of the Nastaleeq font writing. To identify printed Urdu text in images, we have proposed an encoder-decoder based hybrid deep learning approach with Convolutional Neural Network (CNN) for feature extraction part, bi-d