A Complete Off-line Sindhi Handwritten Text Recognition: A Survey

Author(s)

Mr. Muhammad Shafique Kalmarvi , Intzar Ali Lashari , Maryam Bibi , Dil Nawaz Hakro , Akhtar H. Jalbani ,

Download Full PDF Pages: 131-138 | Views: 353 | Downloads: 105 | DOI: 10.5281/zenodo.3469359

Volume 6 - April 2017 (04)

Abstract

Artificial Intelligence is finding ways to make machines more intelligent and work like human being. Image processing, Natural language processing and Optical Character Recognition (OCR) are the active fields of computer vision, where the computers are made more versatile to understand, read and write natural human languages spoken around the word. Optical Characters Recognition (OCR) and Intelligent Characters Recognition (ICR) differ in recognizing printed and handwritten characters respectively. Intelligent Characters Recognition (ICR) is an active field in which handwritten characters are converted into editable text from the image, and remain the point of interest for researchers around the world. Many of the languages of the world possess their Intelligent Characters Recognition (ICR) or their ICR systems are in process. Latin scripts possess their ICR and are near to perfect whereas Arabic script and its adopting languages need more attention for the development of ICR systems. Sindhi language is a language having rich background and culture of more than 5000 years still lacks the ICR system. As there is no any handwritten recognition system for Sindhi Language, so there is no handwritten database is available for testing and training. Enhanced segmentation and feature extraction algorithms are needed which can fully suit with Sindhi script. An integrated handwritten system will be the output of this system in which handwritten text is recognized and editable text will be available for the further processing

Keywords

Sindhi ICR, Computer Vision, Image Processing, Handwritten Text Recognition 

References

  1. Hakro, D. N.; Talib, A. Z.; Bhatti, Z. & Mojai, G. N.: A Study of Sindhi Related and Arabic Script Adapted Languages Recognition. In: Sindh University Research Journal (Science Series) 46 (2014), Nr. 3
  2. Ramdan, J.; Omar, K.; Faidzul, M. & Mady, A.: Arabic Handwriting Data Base for Text Recognition. In: Procedia Technology 11 (2013), Nr. 0, S. 580 – 584
  3. Wahab, M.; Amin, H. & Ahmed, F.: Shape analysis of Pashto script and creation of image database for OCR. In Emerging Technologies, 2009. ICET 2009. International Conference on., 2009, S. 287-290
  4. Abu-Ain, T.; Abdullah, S. N. H. S.; Bataineh, B.; Abu-Ain, W. & Omar, K.: Text Normalization Framework for Handwritten Cursive Languages by Detection and Straightness the Writing Baseline In: Procedia Technology 11 (2013), Nr. 0, S. 666 – 671
  5. Pervouchine, V.; Leedham, G. & Melikhov, K.: Handwritten character skeletonisation for forensic document analysis. In:: . : Proceedings of the 2005 ACM symposium on Applied computing., 2005, S. 754—758
  6. Shaikh, N.; Mallah, G. & Shaikh, Z.: Character Segmentation of Sindhi, an Arabic Style Scripting Language, using Height Profile Vector. In: Australian Journal of Basic and Applied Sciences 3 (2009), Nr. 4, S. 4160—4169
  7. Assabie, Y. & Bigun, J.: Offline handwritten Amharic word recognition. In: Pattern Recognition Letters 32 (2011), Nr. 8, S. 1089 – 1099
  8. Basu, S.; Das, N.; Sarkar, R.; Kundu, M.; Nasipuri, M. & Basu, D. K.: A hierarchical approach to recognition of handwritten Bangla characters. In: Pattern Recognition 42 (2009), Nr. 7, S. 1467 – 1484
  9. Amin, A.: Off-line Arabic character recognition: the state of the art. In: Pattern Recognition 31 (1998), Nr. 5, S. 517 – 530
  10. Newell, A. J. & Griffin, L. D.: Writer identification using oriented Basic Image Features and the Delta encoding. In: Pattern Recognition 47 (2014), Nr. 6, S. 2255 – 2265
  11. Ahmad, R.; Amin, S. & Khan, M.: Scale and rotation invariant recognition of cursive Pashto script using SIFT features merging Technologies (ICET), 2010 6th International Conference on., 2010, S. 299-303
  12. D. N. Hakro, I. A. I.; Talib, A. Z.; Bhatti, Z. & Mojai,: Multilingual Text Image Database for OCR Sindh Univ. Res. Jour. (Sci. Ser.) Vol.47 (1): pp 181-186 (2015)
  13. Mukherji, P. & Rege, P.: Shape Feature and Fuzzy Logic Based Offline Devnagari Handwritten Optical Character Recognition. In: Journal of Pattern Recognition Research 5 (2010), Nr. 1, S. 52—68
  14. Parvez, M. T. & Mahmoud, S. A.: Arabic handwriting recognition using structural and syntactic pattern attributes. In: Pattern Recognition 46 (2013), Nr. 1, S. 141 – 154
    [xv]- Ganapathy, V. & Liew, K.: Handwritten character recognition using multiscale neural network training technique. In: World Academy of Science, Engineering and Technology 39 (2008), S. 32—37
  15. Wen, Y. & He, L.: A classifier for Bangla handwritten numeral recognition. In: Expert Systems with Applications 39 (2012), Nr. 1, S. 948 – 953
  16. Hewavitharana, S. & Fernando, H.: A two stage classification approach to Tamil handwriting recognition. In: Tamil Internet, (2002), S. 118—124
  17. Nizamani, A. & Janjua, N.: Sindhi OCR using Back Propagation Neural Network. In: International Journal of Computer Science and Security (IJCSS) 1 (2011), Nr. 3, S. 1
  18. Zamora-Martínez, F., Frinken, V., España-Boquera, S., Castro-Bleda, M., Fischer, A. and Bunke, H. (2014). Neural network language models for off-line handwriting recognition, Pattern Recognition 47(4): 1642 – 1652.
  19. Sarfraz, M.; Nawaz, S. & Al-Khuraidly, A.: Offline Arabic text recognition system. In:: . : Geometric Modeling and Graphics, 2003. Proceedings. 2003 International Conference on., 2003, S. 30-35
    [xxi]- Al-Taani, A. & Al-Haj, S.: Recognition of On-line Arabic Handwritten Characters Using Structural Features. In: Journal of Pattern Recognition Research 5 (2010), Nr. 1, S. 23—37
  20. Saha, S. & Som, T.: Handwritten Character Recognition by Using Neural-Network and Euclidean Distance Metric. In: IJCSIC-International Journal of Computer Science and Intelligent Computing 2 (2010), Nr. 1
  21. Santosh, K.; Lamiroy, B. & Wendling, L.: Symbol recognition using spatial relations. In: Pattern Recognition Letters 33 (2012), Nr. 3, S. 331 – 341
  22. Tao, D.; Liang, L.; Jin, L. & Gao, Y.: Similar handwritten Chinese character recognition by kernel discriminative locality alignment. In: Pattern Recognition Letters 35 (2014), Nr. 0, S. 186 – 194
  23. D. N. Hakro, I. A. I.; Talib, A. Z.; Bhatti, Z. & Mojai, G. N.: Issues and Challenges in Sindhi OCR. In: Sindh University Research Journal (Science Series) 46 (2014), Nr. 2

Cite this Article: