Handwritten English Character Recognition and translate English to Devnagari Words

Authors(7) :-Shivali Parkhedkar, Shaveri Vairagade, Vishakha Sakharkar, Bharti Khurpe, Arpita Pikalmunde, Amit Meshram, Prof. Rakesh Jambhulkar

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.

Authors and Affiliations

Shivali Parkhedkar
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India
Shaveri Vairagade
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India
Vishakha Sakharkar
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India
Bharti Khurpe
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India
Arpita Pikalmunde
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India
Amit Meshram
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India
Prof. Rakesh Jambhulkar
Department of Information Technology , RTMNU/MIET Bhandara, Maharashtra, India

HCR(Handwritten Character Recognition), QBT(Querry By Text), QBS(Querry By String), DTW(Dynamic Time Wrapping), ICA(Independent Component Analysis).

  1. J Almazan, A. Gordo, A. Fornes, and E. Valveny, “Handwritten word spotting with corrected attributes,” in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 1017–1024.
  2. R Manmatha and J. Rothfeder, “A scale space approach for automatically segmenting words from historical handwritten documents,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1212–1225, Aug. 2005.
  3. L Neumann and J. Matas, “Real-time scene text localization and recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 3538–3545.
  4. L Neumann and J. Matas, “Scene text localization and recognition with oriented stroke detection,” in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 97–104.
  5. A Bissacco, M. Cummins, Y. Netzer, and H. Neven, “PhotoOCR: Reading text in uncontrolled conditions,” in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 785–792.
  6. A Fischer, A. Keller, V. Frinken, and H. Bunke, “HMM-based word spotting in handwritten documents using subword models,” in Proc. 20th Int. Conf. Pattern Recog., 2010, pp. 3416–3419.
  7. V Frinken, A. Fischer, R. Manmatha, and H. Bunke, “A novel word spotting method based on recurrent neural networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 2, pp. 211–224, Feb. 2012.
  8. R Manmatha, C. Han, and E. M. Riseman, “Word spotting: A new approach to indexing handwriting,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 1996, pp. 631–637.
  9. T Rath, R. Manmatha, and V. Lavrenko, “A search engine for historical manuscript images,” in Proc. 27th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2004, pp. 369–376.
  10. T. Rath and R. Manmatha, “Word spotting for historical documents,” Int. J. Document Anal. Recog., vol. 9, pp. 139–152, 2007.
  11. J. A. Rodriguez-Serrano and F. Perronnin, “Local gradient histogram features for word spotting in unconstrained handwritten documents,” presented at the Int. Conf. Frontiers Handwriting Recognition, Montreal, QC, Canada, 2008.
  12. J. A. Rodriguez-Serrano and F. Perronnin, “A model-based sequence similarity with application to handwritten wordspotting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2108–2120, Nov. 2012.
  13. S. Espana-Bosquera, M. Castro-Bleda, J. Gorbe-Moya, and~ F. Zamora-Martinez, “Improving offline handwritten text recognition with hybrid HMM/ANN models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 4, pp. 767–779, Apr. 2011.
  14. I. Yalniz and R. Manmatha, “An efficient framework for searching text in noisy documents,” in Proc. 10th IAPR Int. Workshop Document Anal. Syst., 2012, pp. 48–52.
  15. K. Wang, B. Babenko, and S. Belongie, “End-to-end scene text recognition,” in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 1457–1464.
  16. A. Vinciarelli and S. Bengio, “Offline cursive word recognition using continuous density hidden Markov models trained with PCA or ICA features,” in Proc. 16th Int. Conf. Pattern Recog., 2002, pp. 81–84.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 142-151
Manuscript Number : CSEIT19528
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Shivali Parkhedkar, Shaveri Vairagade, Vishakha Sakharkar, Bharti Khurpe, Arpita Pikalmunde, Amit Meshram, Prof. Rakesh Jambhulkar, "Handwritten English Character Recognition and translate English to Devnagari Words", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.142-151, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT19528
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