Skip to main content

Advertisement

Log in

FPGA architecture for text extraction from images

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Image processing is a computationally intensive operation and it is typically done in software using CPU processing power that is readily available these days. The applications that make use of image processing require expensive and powerful CPUs to perform real-time operations. Hence a low cost FPGA based image processing solution becomes useful. This eliminates the need for powerful CPUs and at the same time real-time processing can be achieved relatively easier. This paper proposes an algorithm for extracting text information from images. With the help of FPGA, the pixels can be pipelined or processed in parallel in order to achieve increased processing speed. The gray scale conversion is done on the input image followed by image binarization. In order to extract text from input image, morphological close operation and connected component analysis are performed. The ultimate aim and motivation of this paper is detected and extract the words and letters from the input image with efficient hardware architecture. Simulation is done using VHDL coding and the analysis and synthesis results are carried out using the device XC7VX330T of Virtex7 family. FPGA provides higher flexibility since the architecture can be easily upgraded to meet the requirement. It is observed that area and power minimization is obtained by implementing an optimized design using this algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Shivakumara, P., Phan, T.Q., Tan, C.L.: A Laplacian approach to multi-oriented text detection in video. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 412–419 (2011)

    Article  Google Scholar 

  2. Kim, W., Kim, C.: A new approach for overlay text detection and extraction from complex video scene. IEEE Trans. Image Process. 18(2), 415–423 (2009)

    MathSciNet  MATH  Google Scholar 

  3. Wang, L., Song, W., Liu, P.: Link the remote sensing big data to the image features via wavelet transformation. Cluster Comput. 19(2), 793–810 (2016)

    Article  Google Scholar 

  4. Yu, C., Song, Y., Meng, Q., Zhang, Y., Liu, Y.: Text detection and recognition in natural scene with edge analysis. IET Comput. Vis. 9(4), 603–613 (2014)

    Article  Google Scholar 

  5. Chen, X.L., Yang, J., Zhang, J., Waibel, A.: Automatic detection and recognition of signs from natural scenes. IEEE Trans. Image Process. 13(1), 87–99 (2004)

    Article  Google Scholar 

  6. Johnston, C.T., Bailey, D.G.: FPGA implementation of a single pass connected components algorithm. IEEE Trans. Image Process. 13(1), 87–99 (2008)

    Google Scholar 

  7. Hedberg, H., Kristensen, F., Öwall, V.: Low-complexity binary morphology architectures with flat rectangular structuring elements. IEEE Trans. Circuits Syst. 55(8), 2216–2225 (2008)

    Article  MathSciNet  Google Scholar 

  8. Koo, H.I., Kim, D.H.: Scene text detection via connected component clustering and nontext filtering. IEEE Trans. Image Process. 22(6), 2296–2305 (2013)

    Article  MathSciNet  Google Scholar 

  9. Ryu, J., Koo, H.I., Cho, N.I.: Language-independent text-line extraction algorithm for handwritten documents. IEEE Signal Process. Lett. 21(9), 1115–1119 (2014)

    Article  Google Scholar 

  10. Chen, M., Wu, W., Yang, X., He, X.: Hidden-Markov-model-based segmentation confidence applied to container code character extraction. IEEE Trans. Intell. Transport. Syst. 12(4), 1147–1156 (2011)

    Article  Google Scholar 

  11. Gupta, N., Banga, V.K.: Localization of text in complex images using haar wavelet transform. Int. J. Innov. Technol. Explor. Eng. 1, 2278–3075 (2012)

    Google Scholar 

  12. Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1480–1500 (2015)

    Article  Google Scholar 

  13. Ranganathan, N., Mehrotra, R., Subramaniam, S.: A high speed systolic architecture for labeling connected components in an image. IEEE Trans. Syst. Man Cybern. 25(3), 415–423 (1995)

    Article  Google Scholar 

  14. Velho, L., Frery, A., Gomes, J.: Image Processing for Computer Graphics and Vision. Springer-Verlag, New York (2009)

    Book  Google Scholar 

  15. Zhou, Y., Panetta, K., Agaian, S., Chen, C.L.P.: (n, k, p)–Gray code for image systems. IEEE Trans. Cybern. 43(2), 515–29 (2013)

    Article  Google Scholar 

  16. Soille, P.: Morphological Image Analysis-Principle and Applications. Springer, Germany (2003)

    MATH  Google Scholar 

  17. Maragos, P.: Morphological filtering for image enhancement and feature detection. The Image and Video Processing Handbook, 2nd edn, pp. 135–156. Elsevier Academic Press, New York (2005)

    Chapter  Google Scholar 

  18. Hea, L., Ren, X., Gao, Q., Zhao, X., Yao, Bin, Chao, Yuyan: The connected-component labeling problem: a review of state-of-the-art algorithms. Pattern Recognit. 70, 25–43 (2017)

    Article  Google Scholar 

  19. Xilinx 7 Series FPGAs Configurable Logic Block User Guide UG474 (v1.7). (2014)

  20. Virtex FPGA series configuration and readback. Xilinx Inc., XAPP138 (v2.8). (2005)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to O. Vignesh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vignesh, O., Mangalam, H. & Gayathri, S. FPGA architecture for text extraction from images. Cluster Comput 22 (Suppl 5), 12137–12146 (2019). https://doi.org/10.1007/s10586-017-1567-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1567-z

Keywords