article

Handwritten Urdu character recognition using one-dimensional BLSTM classifier

Authors:

Saad Bin Ahmed,

Salahuddin Swati,

Muhammad Imran RazzakAuthors Info & Claims

Neural Computing and Applications, Volume 31, Issue 4

Pages 1143 - 1151

https://doi.org/10.1007/s00521-017-3146-x

Published: 01 April 2019 Publication History

Abstract

The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, that is why it nearly shares the similar challenges and complexities but with more intensity. We can categorize Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nasta'liq style, whereas Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nasta'liq handwritten dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. UNHD is publically available and can be download form https://sites.google.com/site/researchonurdulanguage1/databases. We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.

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Cited By

Khan SNazir SKhan H(2023)Analysis of Cursive Text Recognition Systems: A Systematic Literature ReviewACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359260022:7(1-30)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1145/3592600
Tyagi NBhushan B(2023)Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research DirectionsWireless Personal Communications: An International Journal10.1007/s11277-023-10312-8130:2(857-908)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1007/s11277-023-10312-8
Anjum TKhan N(2023)CALText: Contextual Attention Localization for Offline Handwritten TextNeural Processing Letters10.1007/s11063-023-11258-555:6(7227-7257)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1007/s11063-023-11258-5
Show More Cited By

Handwritten Urdu character recognition using one-dimensional BLSTM classifier
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image Neural Computing and Applications

Neural Computing and Applications Volume 31, Issue 4

April 2019

291 pages

ISSN:0941-0643

EISSN:1433-3058

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Copyright © Copyright © 2019 Springer-Verlag London Ltd., part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2019

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Cited By

Khan SNazir SKhan H(2023)Analysis of Cursive Text Recognition Systems: A Systematic Literature ReviewACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359260022:7(1-30)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1145/3592600
Tyagi NBhushan B(2023)Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research DirectionsWireless Personal Communications: An International Journal10.1007/s11277-023-10312-8130:2(857-908)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1007/s11277-023-10312-8
Anjum TKhan N(2023)CALText: Contextual Attention Localization for Offline Handwritten TextNeural Processing Letters10.1007/s11063-023-11258-555:6(7227-7257)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1007/s11063-023-11258-5
Ganai AKhursheed F(2023)Computationally efficient recognition of unconstrained handwritten Urdu script using BERT with vision transformersNeural Computing and Applications10.1007/s00521-023-08976-135:34(24161-24177)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00521-023-08976-1
Yasin NSiddiqi IMoetesum MRauf S(2023)Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive TextDocument Analysis and Recognition – ICDAR 2023 Workshops10.1007/978-3-031-41501-2_6(80-93)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-41501-2_6
Rakshit PChatterjee SHalder CSen SObaidullah SRoy K(2022)Comparative study on the performance of the state-of-the-art CNN models for handwritten Bangla character recognitionMultimedia Tools and Applications10.1007/s11042-022-13909-682:11(16929-16950)Online publication date: 2-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13909-6
Misgar MMushtaq FKhurana SKumar M(2022)Recognition of offline handwritten Urdu characters using RNN and LSTM modelsMultimedia Tools and Applications10.1007/s11042-022-13320-182:2(2053-2076)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.1007/s11042-022-13320-1
Ganai AKhursheed F(2022)A novel holistic unconstrained handwritten urdu recognition system using convolutional neural networksInternational Journal on Document Analysis and Recognition10.1007/s10032-022-00414-725:4(351-371)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10032-022-00414-7
Rafique AIshtiaq M(2022)UOHTD: Urdu Offline Handwritten Text DatasetFrontiers in Handwriting Recognition10.1007/978-3-031-21648-0_34(498-511)Online publication date: 4-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-21648-0_34
Husnain MSaad Missen MMumtaz SKhan DCoustaty MLuqman MOgier JKhattak HAli SSamad A(2021)Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep NetworkComplexity10.1155/2021/43830372021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4383037
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