Volume 16, Issue 3 (12-2019)                   JSDP 2019, 16(3): 116-101 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Miri E, Razavi S M, Mehrshad N. Search Space Reduction for Farsi Printed Subwords Recognition by Position of the Points and Signs. JSDP. 2019; 16 (3) :116-101
URL: http://jsdp.rcisp.ac.ir/article-1-803-en.html
University of Birjand
Abstract:   (368 Views)
In the field of the words recognition, three approaches of words isolation, the overall shape and combination of them are used. Most optical recognition methods recognize the word based on break the word into its letters and then recogniz them. This approach is faced some problems because of the letters isolation dificulties and its recognition accurcy in texts with a low image quality. Therefore, an approach based on none separating recognition could be useful in such cases. 
In methods based on the overall shapes for subword recognition after extraction of subword features usually these features are searched in the image dictionary created in the training phase. Therefore, by considering that we are faced with massive amounts of classes, proposing ways to limit the scope of the search are the main challenges in the overall shape methods. Thus, the information of the overall shape usually is used to reduce the scope search in a hierarchical form.
In this paper, it is tried to reduce the search space of the subwords severely by using a simple and efficient method.  In training phase, training data is grouped based on the location of the points and signs, in the groups where have more than 10 subwords, to reduce the search space, according to the number of elements in the group, by extracting the simple features of horizontal and vertical profiles clustering takes place. In recognition phase, in the first step, by determining the width to height ratio of the subword (with signs and without signs) and the position code of the points and signs, the search scope is limited to subwords with this position code that are within the range of the ratios mentioned. This range would be accepted if the number of subwords in this phase is less than ten. Otherwise, in the next step, by extracting the simple features of the horizontal and vertical profiles of the subwords, the search space will be limited to a number of the closest clusters to this subword that also satisfies the width-to-height ratio. By using the proposed method of this paper, the search space has fallen to an acceptable level.
In this study, a database of 12700 subwords with five Lotus, Zar, Nazanin, Mitra and Yaghut fonts scanned 400 dpi was used. The four Lotus, Zar, Nazanin and Mitra fonts were used in the training phase and in the test phase, Yaghut ​​font is used.
Full-Text [PDF 4256 kb]   (146 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2017/10/24 | Accepted: 2019/06/19 | Published: 2020/01/7 | ePublished: 2020/01/7

References
1. [1] T. Adamek, N. E. Connor, and A. F. Smeaton, "Word matching using single closed contours for indexing Handwritten Historical Documents," International Jurnal of Document Analysis and Recognition, vol. 9, no. 2-4, pp. 153-165, 2007.
2. [2] J. R. Pinales, R. J. Rivas, and M. J. C. Bleda, "Holistic Cursive word recognition based on perceptual features," Pattern Recognition Letters, vol. 28, no. 13, pp. 1600-1609, 1 Oct. 2007.
3. [3] A. Amin, "Recognition of printed arabic text based on global features and decision tree learning techniques," Pattern Recognition, vol. 33, no. 8, pp. 1309-1323, 2000.
4. [4] A. Ebrahimi, ''Using the holistic form of print subwords in retrieving documentary images and recognizing Persian texts'', Ph.D. dissertation, Electron. Eng., Tarbiat Modares Univ., Tehran, 1384.
5. [5] H. Khosravi, E. Kabir, '' Evaluation of methods for recognizing Persian texts based on the holistic form of subwords,'' Iranian Journal of Electrical and Computer Engineering, vol.7, no.4, pp.267-280, 2005.
6. [6] S. Madhvanath, G. Kim, and V. Govindaraju, "Chain code contour processing for handwritten word recognition," IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 21, no. 9, pp. 928-932, Sep. 1999.
7. [7] K. Zagoris, K. Ergina, and N. Papamarkos, "A document image retrieval system," Engineering Application of Artificial Intelligence, vol. 23, no. 6, pp. 872-879, 2010.
8. [8] S. Bai, L. Li, and C. L. Tan, "Keyword spotting in document images through word shape coding," in Proc. 10th International Conference on Document Analysis and Recognition, ICDAR'09, pp. 331-335, 26-29 Jul. 2009.
9. [9] L. Li, S. Lu, and C. L. Tan, "A fast keyword-spotting technique," in Proc. 9th Int. Conference on Document Analysis and Recognition, ICDAR'07, pp.68-72, 23-26 Sep. 2007.
10. [10] S. Lu and C. L. Tan, "Document image retrieval through word shape coding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1913-1918, Nov. 2008.
11. [11] J. A. Rodriguez-Serrano and F. Perronnin, "Handwritten word spotting using hidden markov models and vocabularies," Pattern Recognition, vol. 42, no. 9, pp. 2106-2116, Sep. 2009.
12. [12] T. M. Rath and R. Manmatha, "Word spotting for historical documents," International Jurnal on Document Analysis and Recognition, Vol. 9, no. 2-4, pp. 139-152, Apr. 2007.
13. [13] Y. Lu and C. L. Tan, "Information retrieval in document image databases," IEEE Transactions on nowledge and Data Engineering, Vol. 16, no. 11, pp. 1398-1410, Nov. 2004.
14. [14] A. Ebrahimi and E. Kabir, "A pictorial dictionary for printed farsi sub words," Pattern Recognition Letters, Vol. 29, no. 5, pp. 656-663, 2008.
15. [15] A. Rehman and T. Saba, "Off - line cursive script recognition: current advances, comparisons and remaining problems," Artificial Intelligence Review, vol. 37, no. 4, pp. 261-288, 2012.
16. [16] S. G. Madhvanath and V. Govindaraju, "The role of holistic paradigms in handwritten word recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 149-164, Feb. 2001.
17. [17] L. M. Lorigo and V. Govindaraju, "Off - line arabic handwriting recognition: a survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 712-724, May 2008.
18. [18] S. Mozaffari, K. Faez, V. Märgner and H. Elabed, ''Two-stage lexicon reduction for offline Arabic handwritten word recognition,'' International Journal of Pattern Recognition and Artificial Intelligence, vol. 22, No. 07: pp. 1323-1341, November 2008.
19. [19] H. Davoudi, M. Cheriet and E. Kabir, ''Lexicon reduction of handwritten arabic subwords based on the prominent shape regions,'' International Journal on Document Analysis and Recognition, vol 19, Issue 2, pp 139–153, 2016.
20. [20] S. Bromand, M. Iranpurmobaraka," Handwritten words recognion with new features and reducing the dictionary," Machine Vision And Image Processing, unpublished.
21. [21] H. Davoudi, E. Kabir, ''Using compatible shape descriptor for lexicon reduction of printed farsi subwords," International Journal on Document Analysis and Recognition, vol. 19, Issue 2. pp 139-153, 2016.
22. [22] H. Davoudi, E. Kabir, ''Using compatible shape descriptor for lexicon reduction of printed farsi subwords," Iranian Journal of Electrical and Computer Engineering, vol. 12, Issue1., 2014.
23. [23] F. Fathi, " Extraction of index letters from Persian printed subwords", M.S. thesis, Dept. Electron.Eng., Sahand University of Technology, Tabriz, Iran, 2009.
24. [24] M. Alibaigi, "Persian printed subwords recognition", M.Sc. thesis, Departmet of Electronic Engineering, University of Birjand, Birjand, Iran, 2010.
25. [25] E. Miri, S.M. Razavi, N. Mehrshad, " A simple method for search space reduction in Persian typed subwords recognition," 9th Conference on Machine Vision and Image Processing conference, Shahid Behshti University, Tehran, 2015.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2015 All Rights Reserved | Signal and Data Processing