note

Printed Text Image Database for Sindhi OCR

Authors:

Dil Nawaz Hakro,

Abdullah Zawawi TalibAuthors Info & Claims

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

Article No.: 21, Pages 1 - 18

https://doi.org/10.1145/2846093

Published: 16 May 2016 Publication History

Get Access

Abstract

Document Image Understanding (DIU) and Electronic Document Management are active fields of research involving image understanding, interpretation, efficient handling, and routing of documents as well as their retrieval. Research on most of the noncursive scripts (Latin) has matured, whereas research on the cursive (connected) scripts is still moving toward perfection. Many researchers are currently working on the cursive scripts (Arabic and other scripts adopting it) around the world so that the difficulties and challenges in document understanding and handling of these scripts can be overcome. Sindhi script has the largest extension of the original Arabic alphabet among languages adopting the Arabic script; it contains 52 characters, compared to 28 characters in the original Arabic alphabet, in order to accommodate more sounds for the language. There are 24 differentiating characters with some possessing four dots. For Sindhi OCR research and development, a database is needed for training and testing of Sindhi text images. We have developed a large database containing over 4 billion words and 15 billion characters in 150 various fonts in four font weights and four styles. The database contents were collected from various sources including websites, books, and theses. A custom-built application was also developed to create a text image from a text document that supports various fonts and sizes. The database considers words, characters, characters with spaces, and lines. The database is freely available as a partial or full database by sending an email to one of the authors.

References

[1]

Ashraf AbdelRaouf, Colin Higgins, and Mahmoud Khalil. 2008. A database for Arabic printed character recognition. In Image Analysis and Recognition, Lecture Notes in Computer Science. A. Campilho and M. Kamel (Eds.). Vol. 5112, Springer, Berlin. 567--578.

Digital Library

Google Scholar

[2]

Ghulam Ali Alana. 1993. Sindhi Sooratkhati (4th ed.). Sindhi Language Authority Hyderabad, Sindh.

Google Scholar

[3]

Ghulam Ali Alana. 2004. Sindhi Boli Jo Bunn Bunyad. Sindhi Language Authority Hyderabad. Sindh. Pakistan.

Google Scholar

[4]

Yousef Al-Ohali, Mohamed Cheriet, and Ching Suen. 2003. Databases for recognition of handwritten arabic cheques. Pattern Recognition 36, 1 (2003), 111--121.

Crossref

Google Scholar

[5]

Dil Nawaz Hakro, Imdad A. Ismaili, Abdullah Zawawi Talib, Zeeshani Bhatti, and Ghulam Nabi Mojai. 2014. Issues and challenges in Sindhi OCR. Sindh University Research Journal (Science Series) 46, 2 (2014), 143--152.

Google Scholar

[6]

Madiha Ijaz and S. Sarmad Hussain. 2007. Corpus based urdu lexicon development. In The Proceedings of Conference on Language Technology 2007 (CLT’07), University of Peshawar, Peshawar, Pakistan. Vol. 73. 1--12.

Google Scholar

[7]

Sabri Mahmoud, Irfan Ahmad, Wasfi G. Al-Khatib, Mohammad Alshayeb, Mohammad Tanvir Parvez, Volker Margner, and Gernot A. Fink. 2014. KHATT: An open arabic offline handwritten text database. Pattern Recognition 47, 3 (2014), 1096--1112.

Digital Library

Google Scholar

[8]

Robert Parker, David Graff, Ke Chen, Junbo Kong, and Kazuaki Maeda. 2011. Arabic Gigaword (5th ed. LDC2011T11). Linguistic Data Consortium, Philadelphia University of Pennsylvania.

Google Scholar

[9]

Mohammad Tanvir Parvez and Sabri A. Mahmoud. 2013. Arabic handwriting recognition using structural and syntactic pattern attributes. Pattern Recognition 46, 1 (2013), 141--154.

Digital Library

Google Scholar

[10]

Mario Pechwitz, Samia Snoussi Maddouri, Volker Märgner, Noureddine Ellouze, and Hamid Amiri. 2002. IFN/ENIT database of handwritten arabic words. In Proceedings of of CIFED, 2, Citeseer. 127--136.

Google Scholar

[11]

Mutee U. Rahman. 2010. Towards Sindhi corpus construction. In Conference on Language and Technology (CLT’10). Lahore, Pakistan. 37--45.

Google Scholar

[12]

Rajneesh Rani, Renu Dhir, and G. S. Lehal. 2011. Identification of printed Punjabi words and English numerals using Gabor features. World Academy of Science, Engineering and Technology 73, 1 (2011), 392--395.

Google Scholar

[13]

Fouad Slimane, Rolf Ingold, Slim Kanoun, Adel M. Alimi, and Jean Hennebert. 2009. A new Arabic printed text image database and evaluation protocols. In 10th International Conference on Document Analysis and Recognition, 2009 (ICDAR’09). 946--950.

Digital Library

Google Scholar

[14]

Fouad Slimane, Slim Kanoun, Jean Hennebert, Adel M. Alimi, and Rolf Ingold. 2013. A study on font-family and font-size recognition applied to arabic word images at ultra-low resolution. Pattern Recognition Letters 34, 2 (2013), 209--218.

Digital Library

Google Scholar

Cited By

View all

Siddiqu ABasit ANoor WAsfandyar Khan MSaeed H. Kakar MKhan A(2023)Baseline Isolated Printed Text Image Database for Pashto Script RecognitionIntelligent Automation & Soft Computing10.32604/iasc.2023.03642637:1(875-885)Online publication date: 2023
https://doi.org/10.32604/iasc.2023.036426
Ali ZKhan SKhuram Shahzad MBilal H(2023)A Large-Scale Font-Diverse Sindhi Ligature Recognition System2023 International Conference on Frontiers of Information Technology (FIT)10.1109/FIT60620.2023.00033(132-137)Online publication date: 11-Dec-2023
https://doi.org/10.1109/FIT60620.2023.00033
Bilgin Tasdemir E(2023)Printed Ottoman text recognition using synthetic data and data augmentationInternational Journal on Document Analysis and Recognition10.1007/s10032-023-00436-926:3(273-287)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1007/s10032-023-00436-9
Show More Cited By

Index Terms

Printed Text Image Database for Sindhi OCR
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Document scanning
      2. Optical character recognition

Recommendations

OCR for printed Kannada text to machine editable format using database approach

This paper describes an Optical Character Recognition (OCR) system for printed text documents in Kannada, a South Indian language. The proposed OCR system for the recognition of printed Kannada text, which can handle all types of Kannada characters. The ...
OCR for printed Kannada text to machine editable format using database approach
ICAI'08: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information

This paper describes an Optical Character Recognition (OCR) system for printed text documents in Kannada, a South Indian language. The proposed OCR system for the recognition of printed Kannada text, which can handle all types of Kannada characters. The ...
OCR of printed telugu text with high recognition accuracies
ICVGIP'06: Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing

Telugu is one of the oldest and popular languages of India spoken by more than 66 million people especially in South India. Development of Optical Character Recognition systems for Telugu text is an area of current research.

OCR of Indian scripts is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

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

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2016

Accepted: 01 November 2015

Revised: 01 November 2015

Received: 01 April 2015

Published in TALLIP Volume 15, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Note
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
344
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 01 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Siddiqu ABasit ANoor WAsfandyar Khan MSaeed H. Kakar MKhan A(2023)Baseline Isolated Printed Text Image Database for Pashto Script RecognitionIntelligent Automation & Soft Computing10.32604/iasc.2023.03642637:1(875-885)Online publication date: 2023
https://doi.org/10.32604/iasc.2023.036426
Ali ZKhan SKhuram Shahzad MBilal H(2023)A Large-Scale Font-Diverse Sindhi Ligature Recognition System2023 International Conference on Frontiers of Information Technology (FIT)10.1109/FIT60620.2023.00033(132-137)Online publication date: 11-Dec-2023
https://doi.org/10.1109/FIT60620.2023.00033
Bilgin Tasdemir E(2023)Printed Ottoman text recognition using synthetic data and data augmentationInternational Journal on Document Analysis and Recognition10.1007/s10032-023-00436-926:3(273-287)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1007/s10032-023-00436-9
Sodhar IJalbani AChanna MHakro D(2021)Romanized Sindhi Rules for Text CommunicationMehran University Research Journal of Engineering and Technology10.22581/muet1982.2102.0440:2(298-304)Online publication date: 1-Apr-2021
https://doi.org/10.22581/muet1982.2102.04
Solangi YSolangi ZRaza AShaikh NMallah GShah A(2018)Offline-printed Sindhi Optical Text Recognition: Survey2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS)10.1109/ICETAS.2018.8629110(1-5)Online publication date: Nov-2018
https://doi.org/10.1109/ICETAS.2018.8629110
Khan NAdnan ABasar S(2017)Urdu ligature recognition using multi-level agglomerative hierarchical clusteringCluster Computing10.1007/s10586-017-0916-221:1(503-514)Online publication date: 25-May-2017
https://doi.org/10.1007/s10586-017-0916-2

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

OCR for printed Kannada text to machine editable format using database approach

OCR for printed Kannada text to machine editable format using database approach

OCR of printed telugu text with high recognition accuracies