FPGA architecture for text extraction from images
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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.
Keywords
FPGA Text extraction Morphological operation Connected component analysisReferences
- 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)CrossRefGoogle 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)MathSciNetzbMATHGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)MathSciNetCrossRefGoogle 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)MathSciNetCrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar
- 14.Velho, L., Frery, A., Gomes, J.: Image Processing for Computer Graphics and Vision. Springer-Verlag, New York (2009)CrossRefGoogle 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)CrossRefGoogle Scholar
- 16.Soille, P.: Morphological Image Analysis-Principle and Applications. Springer, Germany (2003)zbMATHGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar
- 19.Xilinx 7 Series FPGAs Configurable Logic Block User Guide UG474 (v1.7). (2014)Google Scholar
- 20.Virtex FPGA series configuration and readback. Xilinx Inc., XAPP138 (v2.8). (2005)Google Scholar