Prefiltration Analysis for Image Recognition Algorithms for the Android Mobile Platform

1. Cherietm, M., Khaarma, N., Liu, C.L., Suen, C.: Character Recognition Systems: A Guide for Students and Practitioners. Wiley, New Jersey (2007)

   Book  Google Scholar 

2. Poovizhi, P.: A Study on preprocessing techniques for the character recognition. Int. J. Open Inf. Technol. 2(12), 21–24 (2014)

   Google Scholar 

3. Schantz, H.F.: The History of OCR. Recognition Technologies Users Association, Boston (1982)

   Google Scholar 

4. Christen, P.: A comparison of personal name matching: techniques and practical issues. In: Joint Computer Science Technical Report Series, pp. 1–14 (2006)

   Google Scholar 

5. Jaro, M.A.: Advances in record linkage methodology as applied to the 1985 census of Tampa Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)

   Article  Google Scholar 

6. Kay, A.: Tesseract is a quirky command-line tool that does an outstanding job. Linux J. 24–29 (2007)

   Google Scholar 

7. Russ, J.C.: The Image Processing Handbook Sixth Edition, Raleigh. Taylor and Francis Group, LLC, North Carolina (2011)

   Google Scholar 

8. Dougherty, G.: Digital Image Processing for Medical Applications. Cambridge University Press, Cambridge (2009)

   Book  Google Scholar 

9. Huang, Z.-K., Chau, K.-W.: A new image thresholding method based on gaussian mixture model. Appl. Math. Comput. 205, 899–907 (2008)

   MathSciNet  MATH  Google Scholar 

10. Chaubey, A.K.: Comparison of the local and global thresholding methods in image segmentation. World J. Res. Rev. 2, 01–04 (2016)

    Google Scholar 

11. Firdous, R., Parveen, S.: Local thresholding techniques in image binarization. Int. J. Eng. Comput. Sci. 3, 4062–4065 (2014)

    Google Scholar 

12. Isheawy, N.A., Hasan, H.: Optical Character Recognition (OCR) system. IOSR J. Comput. Eng. 17(2), 22–26 (2015)

    Google Scholar 

13. Deshpande, S., Shiram R.: Real time text detection and recognition on hand held objects to assist blind people. In: International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE (2016)

    Google Scholar 

14. Springmann, U., Fink, F., Schulz, K.: Automatic quality evaluation and (semi-) automatic improvement of mixed models for OCR on historical documents, CoRR (2016)

    Google Scholar 

15. Chao, S.L., Lin, Y.L.: Gate automation system evaluation: a case of a container number recognition system in port terminals. Marit. Bus. Rev. 2(1), 21–35 (2017)

    Article  MathSciNet  Google Scholar 

16. Islam, N., Islam Z., Noor N.: A survey on optical character recognition system. arXiv preprint arXiv:1710.05703 (2017)

Download references