Prakash Choudhary
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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.
- S. Al-Ma’adeed, D. Elliman, and C. A. Higgins. 2002. A data base for Arabic handwritten text recognition research. In Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition. 485--489. Google Scholar
Digital Library
- Y. Al-Ohali, M. Cheriet, and C. Suen. 2003. Databases for recognition of handwritten Arabic cheques. Pattern Recognition 36, 1 (2003), 111--121.Google ScholarCross Ref
- A. Alaei, P. Nagabhushan, and U. Pal. 2011a. A benchmark Kannada handwritten document dataset and its segmentation. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR’11). 141--145. Google ScholarDigital Library
- A. Alaei, U. Pal, and P. Nagabhushan. 2011b. A new scheme for unconstrained handwritten text-line segmentation. Pattern Recognition 44, 4 (April 2011), 917--928. Google ScholarDigital Library
- A. Alaei, U. Pal, and P. Nagabhushan. 2012. Dataset and ground truth for handwritten text in four different scripts. International Journal of Pattern Recognition and Artificial Intelligence 26, 04 (2012), 1--25.Google ScholarCross Ref
- H. Alamri, J. Sadri, C. Y. Suen, and N. Nobile. 2008. A novel comprehensive database for Arabic offline handwriting recognition. In Proceedings of the 11th International Conference on Frontiers in Handwriting Recognition (ICFHR’08). 664--669.Google Scholar
- S. Belhe, S. Chakravarthy, and A. G. Ramakrishnan. 2009. XML standard for indic online handwritten database. In Proceedings of the International Workshop on Multilingual OCR (MOCR’09). ACM, New York, NY, USA, Article 19, 4 pages. Google ScholarDigital Library
- A. S. Bhaskarabhatla, S. Madhvanath, M. N. S. S. K. Pavan Kumar, A. Balasubramanian, and C. V. Jawahar. 2004. Representation and annotation of online handwritten data. In Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR-9’04). 136--141. Google ScholarDigital Library
- S. Bhaskarabhatla and S. Madhvanath. 2004. Experiences in collection of handwriting data for online handwriting recognition in indic scripts. In Proceedings of the 4th International Conference Linguistic Resources and Evaluation (LREC’04). 2223--2226.Google Scholar
- U. Bhattacharya and B. Chaudhuri. 2009. Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 3 (March 2009), 444--457. Google ScholarDigital Library
- Census. 2001. (2001). http://www.censusindia.gov.in/2001-common/censusdataonline.html.Google Scholar
- P. Choudhary, N. Nain, and M. Ahmed. 2015. A unified approach for development of Urdu corpus for OCR and demographic purpose. In Proceedings of the 7th International Conference on Machine Vision (ICMV’15), Vol. 9445. 1--5.Google Scholar
- L. Deng. 2012. The MNIST database of handwritten digit images for machine learning research {best of the web}. IEEE Signal Processing Magazine 29, 6 (Nov. 2012), 141--142.Google Scholar
- R. I. M. Elanwar, M. A. Rashwan, and S. A. Mashali. 2010. OHASD: The first online Arabic sentence database handwritten on tablet PC. In Proceedings of the World Academy of Science, Engineering and Technology (WASET’10), International Conference on Signal and Image Processing (ICSIP’10) 4, 12 (2010), 585--590.Google Scholar
- D. Elliman and N. Sherkat. 2001. A truthing tool for generating a database of cursive words. In Proceedings of the 6th International Conference on Document Analysis and Recognition, 2001. 1255--1262. Google ScholarDigital Library
- B. Gatos, N. Stamatopoulos, and G. Louloudis. 2009. ICDAR 2009 handwriting segmentation contest. In Proceedings of 10th International Conference on Document Analysis and Recognition (ICDAR’09). 1393--1397. Google ScholarDigital Library
- B. Gatos, N. Stamatopoulos, and G. Louloudis. 2010. ICFHR 2010 handwriting segmentation contest. In Proceedings of 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR’10). 737--742. Google ScholarDigital Library
- S. Godara, N. Nain, and M. Ahamed. 2014. Handwritten Urdu script segmentation using hybrid approach. In Proceedings of the DAR 2014 Satellite Workshop of ICVGIP 2014 on Document Analysis and Recognition, 2014.Google Scholar
- E. Grosicki, M. Carr, E. Augustin, and F. Prłteux. 2006. La campagne d’valuation RIMES pour la reconnaissance de courriers manuscrits. In Actes 9me Colloque International Francophone sur lEcrit et le Document (CIFED’06). Fribourg, Suisse, 61--66.Google Scholar
- I. Guyon, L. Schomaker, R. Plamondon, M. Liberman, and S. Janet. 1994. UNIPEN project of online data exchange and recognizer benchmarks. In Proceedings of the 12th IAPR International Conference on Computer Vision and Image Processing, Vol. 2. 29--33.Google Scholar
- P. J. Haghighi, N. Nobile, C. L. He, and C. Y. Suen. 2009. A new large-scale multi-purpose handwritten Farsi database. In Proceedings of the 6th International Conference on Image Analysis and Recognition (ICIAR’09). Springer-Verlag, Berlin, 278--286. Google ScholarDigital Library
- J. J. Hull. 1994. A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 5 (May 1994), 550--554. Google ScholarDigital Library
- N. Ide. 1998b. Corpus encoding standard: SGML guidelines for encoding linguistic corpora. In Proceedings of the 1st International Language Resources and Evaluation Conference. 463--470.Google Scholar
- E. Indermhle, M. Liwicki, and H. Bunke. 2010. IAMonDo-database: An online handwritten document database with non-uniform contents. In Proceedings of the International Workshop on Document Analysis Systems. 97--104. Google ScholarDigital Library
- C. V. Jawahar, A. Balasubramanian, M. Meshesha, and A. M. Namboodiri. 2009. Retrieval of online handwriting by synthesis and matching. Pattern Recognition 42, 7 (2009), 1445--1457. Google ScholarDigital Library
- T. Kasar, D. Kumar, M. N. Anil Prasad, D. Girish, and A. G. Ramakrishnan. 2011. MAST: Multi-script annotation toolkit for scenic text. In Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data. ACM, New York, NY, Article 14, 8 pages. Google ScholarDigital Library
- D. Khanduja, N. Nain, and S. Panwar. 2013. A hybrid feature extraction algorithm for devanagari script. ACM Transactions on Asian Low-Resource Language Information Processing 15, 1, 105--111. Google ScholarDigital Library
- N. Kharma, M. Ahmed, and R. Ward. 1999. A new comprehensive database of handwritten arabic words, numbers, and signatures used for OCR testing. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Vol. 2. 766--768.Google Scholar
- H. Khosravi and E. Kabir. 2007. Introducing a very large dataset of handwritten Farsi digits and a study on their varieties. Pattern Recognition Letters 28, 10 (2007), 1133--1141. Google ScholarDigital Library
- D. H. Kim, Y. S. Hwang, S. T. Park, E. J. Kim, S. H. Paek, and S. Y. Bang. 1993. Handwritten Korean character image database PE92. In Proceedings of the 2nd International Conference on Document Analysis and Recognition. 470--473.Google Scholar
- A. Kumar, A. Balasubramanian, A. Namboodiri, and C. V. Jawahar. 2006. Model-based annotation of online handwritten datasets. In Proceedings of the International Workshop on Frontiers in Handwriting Recognition (IWFHR’06). Universit de Rennes, La Baule, Centre de Congreee Atlantia, France.Google Scholar
- S. Kumar. 2010. An analysis of irregularities in Devanagari script writing: A machine recognition perspective. International Journal of Computer Science Engineering 2, 2 (2010), 274--279.Google Scholar
- Y. Li, Y. Zheng, D. Doermann, S. Jaeger, and Yi Li. 2008. Script-independent text line segmentation in freestyle handwritten documents. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 8 (Aug. 2008), 1313--1329. Google ScholarDigital Library
- L. Likforman-Sulem, A. Zahour, and B. Taconet. 2007. Text line segmentation of historical documents: A survey. International Journal of Document Analysis and Recognition (IJDAR) 9, 2--4 (2007), 123--138. Google ScholarDigital Library
- C.-L. Liu, F. Yin, D.-H. Wang, and Q.-F. Wang. 2011. CASIA online and offline chinese handwriting databases. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR’11), 2011. 37--41. Google ScholarDigital Library
- G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis. 2009. Text line and word segmentation of handwritten documents. Pattern Recognition 42, 12 (2009), 3169--3183. New Frontiers in Handwriting Recognition. Google ScholarDigital Library
- S. A. Mahmoud, I. Ahmad, M. Alshayeb, and W. G. Al-Khatib. 2011. A database for offline arabic handwritten text recognition. In Image Analysis and Recognition, Mohamed Kamel and Aurlio Campilho (Eds.). Lecture Notes in Computer Science, Vol. 6754. Springer, Berlin, 397--406. Google ScholarDigital Library
- V. Margner and H. El Abed. 2009. ICDAR 2009 Arabic handwriting recognition competition. In Proceedings of 10th International Conference on Document Analysis and Recognition, 2009. 1383--1387. Google ScholarDigital Library
- U.-V. Marti and H. Bunke. 2002. The IAM-database: An English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5, 1 (2002), 39--46.Google ScholarCross Ref
- I. B. Messaoud and H. E. Abed. 2010. Automatic annotation for handwritten historical documents using Markov models. In Proceedings of the International Conference on Frontiers in Handwriting Recognition (ICFHR’10). 381--386. Google ScholarDigital Library
- S. Mozaffari, H. El Abed, V. Margner, K. Faez, and A. Amirshahi. 2008. IfN/Farsi-database: A database of Farsi handwritten city names. In Proceedings of the 11th International Conference on Frontiers in Handwriting Recognition (ICFHR’08). 397402.Google Scholar
- M. Nakagawa, T. Higashiyama, Y. Yamanaka, S. Sawada, L. Higashigawa, and K. Akiyama. 1997. Online handwritten character pattern database sampled in a sequence of sentences without any writing instructions. In Proceedings of the 4th International Conference on Document Analysis and Recognition, 1997., Vol. 1. 376--381. Google ScholarDigital Library
- M. Nakagawa and K. Matsumoto. 2004. Collection of online handwritten Japanese character pattern databases and their analyses. Document Analysis and Recognition 7, 1 (2004), 69--81. Google ScholarDigital Library
- B. Nethravathi, C. P. Archana, K. Shashikiran, A. G. Ramakrishnan, and V. Kumar. 2010. Creation of a huge annotated database for Tamil and Kannada OHR. In Proceedings of the International Conference on Frontiers in Handwriting Recognition (ICFHR’10). 415--420. Google ScholarDigital Library
- S. Panwar and N. Nain. 2014. A novel segmentation methodology for cursive handwritten documents. IETE Journal of Research 60, 6 (2014), 432--439.Google ScholarCross Ref
- S. Panwar, N. Nain, S. Saxena, and P. C. Gupta. 2013. Language adaptive methodology for handwritten text line segmentation. In Computer Analysis of Images and Patterns, Richard Wilson, Edwin Hancock, Adrian Bors, and William Smith (Eds.). Lecture Notes in Computer Science, Vol. 8047. Springer, Berlin, 344--351.Google Scholar
- M. Pechwitz, S. S. Maddouri, V. Mrgner, N. Ellouze, and H. Amiri. 2002. IFN/ENIT - database of handwritten Arabic words. In Francophone International Conference on Writing and Document (CIFED'02). Hammamet, Tunisia, 129--136.Google Scholar
- S. T. Piantadosi. 2014. Zipfs word frequency law in natural language: A critical review and future directions. Psychonomic Bulletin Review 21, 5 (2014), 1112--1130.Google ScholarCross Ref
- A. Raza, I. Siddiqi, A. Abidi, and F. Arif. 2012. An unconstrained benchmark Urdu handwritten sentence database with automatic line segmentation. In Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR’12). IEEE Computer Society, Washington, DC, 491--496. Google ScholarDigital Library
- B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. 2008. LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77, 1--3 (2008), 157--173. Google ScholarDigital Library
- R. Safabaksh, A. R. Ghanbarian, and G. Ghiasi. 2013. HaFT: A handwritten Farsi text database. In Proceedings of the 8th Iranian Conference on Machine Vision and Image Processing (MVIP’13). 89--94.Google Scholar
- M. W. Sagheer, C.-L. He, N. Nobile, and C. Suen. 2009. A new large urdu database for offline handwriting recognition. In Proceedings of International Conference on Image Analysis and Processing (ICIAP’09), Pasquale Foggia, Carlo Sansone, and Mario Vento (Eds.). Lecture Notes in Computer Science, Vol. 5716. Springer, Berlin, 538--546. Google ScholarDigital Library
- T. Saito, H. Yamada, and K. Yamamoto. 1985. On the data base ETL9 of handprinted characters in JIS Chinese characters and its analysis (in Japanese). Transactions of the IECE Japan J68-D(4) (1985), 757--764.Google Scholar
- R. Sarkar, N. Das, S. Basu, M. Kundu, M. Nasipuri, and Dk. Basu. 2012. CMATERdb1: A database of unconstrained handwritten Bangla and Bangla-English mixed script document image. International Journal of Document Analysis and Recognition 15, 1 (March 2012), 71--83. Google ScholarDigital Library
- E. Saund, J. Lin, and P. Sarkar. 2009. PixLabeler: User interface for pixel-level labeling of elements in document images. In Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR’09). IEEE Computer Society, Washington, DC, 646--650. Google ScholarDigital Library
- F. Slimane, R. Ingold, S. Kanoun, A. M. Alimi, and J. Hennebert. 2009. A new arabic printed text image database and evaluation protocols. In Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR’09). 946--950. Google ScholarDigital Library
- N. Stamatopoulos, B. Gatos, G. Louloudis, U. Pal, and A. Alaei. 2013. ICDAR 2013 handwriting segmentation contest. In Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR’13). 1402--1406. Google ScholarDigital Library
- M. Sthrenberg. 2012. The TEI and current standards for structuring linguistic data. Journal of the Text Encoding Initiative 3 (Nov. 2012), 1--14.Google ScholarCross Ref
- T. Su, T. Zhang, and D. Guan. 2007. Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text. International Journal of Document Analysis and Recognition (IJDAR) 10, 1 (2007), 27--38. Google ScholarDigital Library
- S. Sutat and L. Methasate. 2004. Thai handwritten character corpus. IEEE International Symposium on Communications and Information Technology 1 (Oct 2004), 486--491.Google Scholar
- C. Viard-Gaudin, P. M. Lallican, S. Knerr, and P. Binter. 1999. The IRESTE on/off (IRONOFF) dual handwriting database. In Proceedings of the 5th International Conference on Document Analysis and Recognition, 1999 (ICDAR’99). 455--458. Google ScholarDigital Library
- R. Wilkinson. 1992. The first census optical character recognition systems. In The U.S. Bureau of Census and the National Institute of Standards and Technology (Tech. Rep. NISTIR 4912, National Institute of Standards and Technology.). Gaithersburg, MD, 1--372.Google Scholar
- F. Yin and C.-L. Liu. 2009. Handwritten Chinese text line segmentation by clustering with distance metric learning. Pattern Recognition 42, 12 (2009), 3146--3157. New Frontiers in Handwriting Recognition. Google ScholarDigital Library
- F. Yin, Q.-F. Wang, and C.-L. Liu. 2009. A tool for ground-truthing text lines and characters in offline handwritten Chinese documents. In Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR’09). 951--955. Google ScholarDigital Library
- M. Ziaratban, K. Faez, and F. Bagheri. 2009. FHT: An unconstraint Farsi handwritten text database. In Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR’09). IEEE Computer Society, Washington, DC, 281--285. Google ScholarDigital Library
Index Terms
- A Four-Tier Annotated Urdu Handwritten Text Image Dataset for Multidisciplinary Research on Urdu Script
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