Evaluation of cursive and non-cursive scripts using recurrent neural networks. Issue 3 (April 2016)

• Record Type: Journal Article
• Authors: Ahmed, Saad; Naz, Saeeda; Razzak, Muhammad; Rashid, Shiekh; Afzal, Muhammad; Breuel, Thomas
• Is Part Of: Neural computing & applications. Volume 27:Issue 3(2016)
• Page Start: 603
• Page End: 613
• Publication Date: 2016-04
• Subjects: Cursive and non-cursive scripts -- Bidirectional long short-term memory networks -- Recurrent neural network -- Connectionist temporal classification -- Synthetic Urdu; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1881-4
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Character recognition has been widely used since its inception in applications involved processing of scanned or camera-captured documents. There exist multiple scripts in which the languages are written. The scripts could broadly be divided into cursive and non-cursive scripts. The recurrent neural networks have been proved to obtain state-of-the-art results for optical character recognition. We present a thorough investigation of the performance of recurrent neural network (RNN) for cursive and non-cursive scripts. We employ bidirectional long short-term memory (BLSTM) networks, which is a variant of the standard RNN. The output layer of the architecture used to carry out our investigation is a special layer called connectionist temporal classification (CTC) which does the sequence alignment. The CTC layer takes as an input the activations of LSTM and aligns the target labels with the inputs. The results were obtained at the character level for both cursive Urdu and non-cursive English scripts are significant and suggest that the BLSTM technique is potentially more useful than the existing OCR algorithms.