Text extraction and character recognition in manuscripts are a very important part of image processing and pattern recognition. The recently proposed Gamma Correction Method (GCM) is a popular method used in the processing of a color image and in particular in the extraction of text from a complex image. However, The GCM consumes a lot of time to extract the text and this is because of the complex calculations of different operations in the different blocks of GCM which is not suitable to real-time applications. In this paper, we propose an efficient Gamma Correction Method acceleration. Our proposal allows to minimize the GCM execution time while ensuring the same reliability of the initial version. The experimental results show an important optimization compared to the literature.
This paper presents hardware (HW) architecture for fast parallel computation of Gray Level Cooccurrence Matrix (GLCM) in high throughput image analysis applications. GLCM has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the GLCM help understand the details about the overall image content. However, the calculation of GLCM is very computationally intensive. In this paper, an FPGA accelerator for fast calculation of GLCM is designed and implemented. We propose an FPGA-based architecture for parallel computation of symmetric cooccurrence matrices. This architecture was implemented on a Xilinx Zedboard and Virtex 5 FPGAs using Vivado HLS. The performance is then compared against other implementations. The validation results show an optimization on the order of 33% in latency number by contribution to the literature implementation.
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