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UrduDeepNet: offline handwritten Urdu character recognition using deep neural network. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06144-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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ul Sehr Zia N, Naeem MF, Raza SMK, Khan MM, Ul-Hasan A, Shafait F. A convolutional recursive deep architecture for unconstrained Urdu handwriting recognition. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06498-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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S D, G UD. Study on Automated Approach to Recognize Characters for Handwritten and Historical Document. ACM T ASIAN LOW-RESO 2021. [DOI: 10.1145/3396167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Script recognition is the mechanism of automatic script analysis and recognition whereby intensive study has been carried out and a significant amount of papers on this problem have been released over the past. But there are still a few issues to be solved, particularly in Indian historical manuscripts. This literature examines the Script recognition with reference to multi-script document and different historical scripts such as Kurdish-Latin, Devanagari, Grantha, Arabic handwritten characters, Bangladesh, Devanagari and Gurumukhi, ancient Chinese, Arabic, Nam Character, Greek, Nastalique Urdu, Georgian handwritten, Nandinagari, and Hebrew, which provide the course of study that focuses on the framework for script recognition. This review concentrates on scope of prediction, dataset type, the methods used for data preprocessing, and measures of performance used for analysis. On the basis of this survey, Current research constraints have been recognized and future study specifications are emphasized in the area of modeling historical manuscripts.
CCS Concepts:
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Affiliation(s)
- Dhivya S
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Usha Devi G
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Ahmadian A, Fouladi K, Araabi BN. Model-based Persian calligraphy synthesis via learning to transfer templates to personal styles. INT J DOC ANAL RECOG 2020. [DOI: 10.1007/s10032-020-00353-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kaur S, Bawa S, Kumar R. A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts. Artif Intell Rev 2020. [DOI: 10.1007/s10462-019-09720-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Rizvi SSR, Sagheer A, Adnan K, Muhammad A. Optical Character Recognition System for Nastalique Urdu-Like Script Languages Using Supervised Learning. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001419530045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature, etc. make the Urdu language too complex to obtain accurate OCR results. In this study, supervised learning-based OCR system is proposed for Nastalique Urdu language. The proposed system evaluations under a variety of experimental settings apprehend 98.4% training results and 97.3% test results, which is the highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help in developing more accurate Urdu OCR’s software systems in the future.
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Affiliation(s)
- S. S. R. Rizvi
- Department of Computer Science and IT, The University of Lahore 54000, Pakistan
- School of Computer Sciences, NCBA&E 54000, Pakistan
| | - A. Sagheer
- School of Computer Sciences, NCBA&E 54000, Pakistan
| | - K. Adnan
- School of Computer Sciences, NCBA&E 54000, Pakistan
| | - A. Muhammad
- Department of Computer Science and IT, The University of Lahore 54000, Pakistan
- School of Computer Sciences, NCBA&E 54000, Pakistan
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Recognition of Urdu Handwritten Characters Using Convolutional Neural Network. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9132758] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.
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Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04069-0] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abstract
This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.
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Naz S, Umar AI, Ahmad R, Siddiqi I, Ahmed SB, Razzak MI, Shafait F. Urdu Nastaliq recognition using convolutional–recursive deep learning. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.02.081] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Naz S, Umar AI, Ahmed R, Razzak MI, Rashid SF, Shafait F. Urdu Nasta'liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks. SPRINGERPLUS 2016; 5:2010. [PMID: 27942426 PMCID: PMC5122597 DOI: 10.1186/s40064-016-3442-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 09/29/2016] [Indexed: 11/10/2022]
Abstract
The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta'liq writing style. Nasta'liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition. We present Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta'liq writing style. Experiments show that MDLSTM attained a recognition accuracy of 98% for the unconstrained Urdu Nasta'liq printed text, which significantly outperforms the state-of-the-art techniques.
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Affiliation(s)
- Saeeda Naz
- National University of Sciences and Technology (NUST), Islamabad, Pakistan ; Department of Information Technology, Hazara University, Mansehra, Pakistan
| | - Arif Iqbal Umar
- Department of Information Technology, Hazara University, Mansehra, Pakistan
| | - Riaz Ahmed
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
| | | | | | - Faisal Shafait
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
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