Ahmed Ben Atitallah^1,*, Mohamed Amin Ben Atitallah^2,3, Yahia Said^4,5, Mohammed Albekairi¹, Anis Boudabous⁶, Turki M. Alanazi¹, Khaled Kaaniche¹, Mohamed Atri⁷

Computer Systems Science and Engineering, Vol.46, No.1, pp. 701-717, 2023, DOI:10.32604/csse.2023.035871

Abstract The challenge faced by the visually impaired persons in their day-to-day lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma… More >

K. O. Mohammed Aarif^1,*, P. Sivakumar²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 275-289, 2022, DOI:10.32604/iasc.2022.022805

Abstract Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In… More >