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A Four-Tier Annotated Urdu Handwritten Text Image Dataset for Multidisciplinary Research on Urdu Script

Authors:

Prakash Choudhary,

Neeta NainAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 15, Issue 4

Article No.: 26, Pages 1 - 23

https://doi.org/10.1145/2857053

Published: 16 May 2016 Publication History

Abstract

This article introduces a large handwritten text document image corpus dataset for Urdu script named CALAM (Cursive And Language Adaptive Methodologies). The database contains unconstrained handwritten sentences along with their structural annotations for the offline handwritten text images with their XML representation. Urdu is the fourth most frequently used language in the world, but due to its complex cursive writing script and low resources, it is still a thrust area for document image analysis. Here, a unified approach is applied in the development of an Urdu corpus by collecting printed texts, handwritten texts, and demographic information of writers on a single form. CALAM contains 1,200 handwritten text images, 3,043 lines, 46,664 words, and 101,181 ligatures. For capturing maximum variance among the words and handwritten styles, data collection is distributed among six categories and 14 subcategories. Handwritten forms were filled out by 725 different writers belonging to different geographical regions, ages, and genders with diverse educational backgrounds. A structure has been designed to annotate handwritten Urdu script images at line, word, and ligature levels with an XML standard to provide a ground truth of each image at different levels of annotation. This corpus would be very useful for linguistic research in benchmarking and providing a testbed for evaluation of handwritten text recognition techniques for Urdu script, signature verification, writer identification, digital forensics, classification of printed and handwritten text, categorization of texts as per use, and so on. The experimental results of some recently developed handwritten text line segmentation techniques experimented on the proposed dataset are also presented in the article for asserting its viability and usability.

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Cited By

Nasir TMalik M(2024)Efficient CRNN: Towards end-to-end low resource Urdu text recognition using depthwise separable convolutions and gated recurrent unitsInformation Processing & Management10.1016/j.ipm.2023.10354461:1(103544)Online publication date: Jan-2024
https://doi.org/10.1016/j.ipm.2023.103544
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
Rahman AGhosh AArora C(2023)UTRNet: High-Resolution Urdu Text Recognition in Printed DocumentsDocument Analysis and Recognition - ICDAR 202310.1007/978-3-031-41734-4_19(305-324)Online publication date: 19-Aug-2023
https://doi.org/10.1007/978-3-031-41734-4_19
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Index Terms

A Four-Tier Annotated Urdu Handwritten Text Image Dataset for Multidisciplinary Research on Urdu Script
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 15, Issue 4

June 2016

173 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/2915955

Editor:
Richard Sproat
Google, Inc., USA

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Copyright © 2016 ACM.

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Publication History

Published: 16 May 2016

Accepted: 01 December 2015

Revised: 01 December 2015

Received: 01 January 2015

Published in TALLIP Volume 15, Issue 4

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Cited By

Nasir TMalik M(2024)Efficient CRNN: Towards end-to-end low resource Urdu text recognition using depthwise separable convolutions and gated recurrent unitsInformation Processing & Management10.1016/j.ipm.2023.10354461:1(103544)Online publication date: Jan-2024
https://doi.org/10.1016/j.ipm.2023.103544
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
Rahman AGhosh AArora C(2023)UTRNet: High-Resolution Urdu Text Recognition in Printed DocumentsDocument Analysis and Recognition - ICDAR 202310.1007/978-3-031-41734-4_19(305-324)Online publication date: 19-Aug-2023
https://doi.org/10.1007/978-3-031-41734-4_19
Mahajan SRani R(2022)Word Level Script Identification Using Convolutional Neural Network Enhancement for Scenic ImagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/350669921:4(1-29)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3506699
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
Khan SKhan HNazir S(2021)Offline Pashto Characters Dataset for OCR SystemsSecurity and Communication Networks10.1155/2021/35438162021Online publication date: 1-Jan-2021
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Sarwar RHassan S(2021)UrduAI: Writeprints for Urdu Authorship IdentificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/347646721:2(1-18)Online publication date: 31-Oct-2021
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Nasir TMalik MShahzad K(2021)MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep LearningIEEE Access10.1109/ACCESS.2021.31107879(124945-124962)Online publication date: 2021
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Nehra MNain NAhmed MModi D(2019)POS Tagging and Structural Annotation of Handwritten Text Image Corpus of Devnagari ScriptEmerging Technologies in Computer Engineering: Microservices in Big Data Analytics10.1007/978-981-13-8300-7_24(286-297)Online publication date: 18-May-2019
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Unar SJalbani AJawaid MShaikh MChandio A(2018)Artificial Urdu Text Detection and Localization from Individual Video FramesMehran University Research Journal of Engineering and Technology10.22581/muet1982.1802.1837:2(429-438)Online publication date: 1-Apr-2018
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