Advertisement

Optical Character and Font Recognizer

  • Manan Rajdev
  • Diksha Sahay
  • Shambhavi Khare
  • Sumita NainanEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)

Abstract

Optical Character and Font Recognizer focuses primarily on building a complete model for document processing. The proposed system recognizes the font style along with the text from an image of certain resolution. The system uses principles of both machine learning and image processing to obtain the desired results. The model uses Contour selection for character extraction and K-Nearest Neighbor approach for character and font recognition. With the assistance of the proposed system using the mentioned techniques, scanned documents can be altered or the font style of a particular document can be known as desired. Many models that perform character recognition are present but a model that performs both character and font recognition with good accuracy is difficult to find. The experiment resulted in 87% overall accuracy for detection of characters.

Keywords

OCR OFR Text recognition K-NN Contour Character-line extraction 

References

  1. 1.
    Grover, S., Arora, K., Mitra, S.K.: Text extraction from document images using edge information. In: 2009 Annual IEEE India Conference, Gujarat, pp. 1–4 (2009)Google Scholar
  2. 2.
    Shi, H., Pavlidis, T.: Font recognition and contextual processing for more accurate text recognition. In: ICDAR 1997, Ulm, Germany, pp. 39–44, August 1997Google Scholar
  3. 3.
    La Manna, S., Colia, A.M., Sperduti, A.: Optical font recognition for multi-font OCR and document processing. In: DEXA 1999 Proceedings of the 10th International Workshop on Database & Expert Systems Applications (1999)Google Scholar
  4. 4.
    Mizan, C., Chakraborty, T., Karmakar, S.: Text recognition using image processing. Int. J. Adv. Res. Comput. Sci. 8(5), 765–768 (2017)Google Scholar
  5. 5.
    Jana, R., Chowdhury, A.R., Islam, M.: Optical character recognition from text image. Int. J. Comput. Appl. Technol. Res. 3(4), 239–243 (2014)Google Scholar
  6. 6.
    Al-Shabi, M.A.M.: Text detection and character recognition using fuzzy image processing. J. Electr. Eng. 57, 258–267 (2006)Google Scholar
  7. 7.
    Bharath, V., Rani, N.S.: A font style classification system for English OCR. In: 2017 International Conference on Intelligent Computing and Control (I2C2), Coimbatore, pp. 1–5 (2017)Google Scholar
  8. 8.
    Zhu, Y., Tan, T., Wang, Y.: Font recognition based on global texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1192–1200 (2001)CrossRefGoogle Scholar
  9. 9.
    Zramdini, A., Ingold, R.: Optical font recognition using typographical features. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 877–882 (1998)CrossRefGoogle Scholar
  10. 10.
    Khoubyari, S., Hull, J.: Font and function word identification in document recognition. Comput. Vis. Image Underst. 63, 66–74 (1996).  https://doi.org/10.1006/cviu.1996.0005CrossRefGoogle Scholar
  11. 11.
    Tiwari, U., Gupta, S., Basudevan, N., Shahani, P.D.: Text extraction from images. Int. J. Electron. Electr. Eng. 7(9), 979–985 (2014). ISSN 0974-2174Google Scholar
  12. 12.
    Coates, A., et al.: Text detection and character recognition in scene images with unsupervised feature learning. In: International Conference on Document Analysis and Recognition, Beijing, pp. 440–445 (2011)Google Scholar
  13. 13.
    Patidar, D., Shah, B.C., Mishra, M.R.: Performance analysis of k nearest neighbors image classifier with different wavelet features. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, pp. 1–6 (2014)Google Scholar
  14. 14.
    Wang, J., Jean, J.: Resolving multifont character confusion with neural networks. Pattern Recogn. 26, 175–187 (1993).  https://doi.org/10.1016/0031-3203(93)90099-ICrossRefGoogle Scholar
  15. 15.
    Kacalak, W., Majewski, M.: Handwriting recognition methods using artificial neural networks. In: Proceedings of the Artificial Neural Networks in Engineering (ANNIE), At St. Louis, Volume: 16 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Manan Rajdev
    • 1
  • Diksha Sahay
    • 1
  • Shambhavi Khare
    • 1
  • Sumita Nainan
    • 1
    Email author
  1. 1.Electronics and TelecommunicationMukesh Patel School of Technology Management & EngineeringMumbaiIndia

Personalised recommendations