Abstract
In the context of constructing an embedded system to help visually impaired people to interpret text, in this paper, an efficient High-level synthesis (HLS) Hardware/Software (HW/SW) design for text extraction using the Gamma Correction Method (GCM) is proposed. Indeed, the GCM is a common method used to extract text from a complex color image and video. The purpose of this work is to study the complexity of the GCM method on Xilinx ZCU102 FPGA board and to propose a HW implementation as Intellectual Property (IP) block of the critical blocks in this method using HLS flow with taking account the quality of the text extraction. This IP is integrated and connected to the ARM Cortex-A53 as coprocessor in HW/SW codesign context. The experimental results show that the HLS HW/SW implementation of the GCM method on ZCU102 FPGA board allows a reduction in processing time by about 89% compared to the SW implementation. This result is given for the same potency and strength of SW implementation for the text extraction.
Highlights
With reference to the World Health Organization, around of the 1.3 billion people live in the world with a few forms of vision impairment [1]
The High-level synthesis (HLS) flow is used to design and implement the complex parts of the Gamma Correction Method (GCM) method as an intellectual property (IPs) block. These blocks are connected as hardware coprocessor to the hardcore ARM Cortex-A53 and implemented on Xilinx ZCU102 Field Programmable Gate Array (FPGA) board in a HW/SW codesign context to increase the reliability and the efficiency of the GCM method for text extraction
The GCM method calculates for each gamma modified image (γ value varies from 0.1 to 10.0 using 0.1 as increment step) four Gray-Level Co-occurrence Matrix (GLCM) matrix, the textural features and the threshold value to extract the text from the image
Summary
With reference to the World Health Organization, around of the 1.3 billion people live in the world with a few forms of vision impairment [1]. In this method the Gray-Level Co-occurrence Matrix (GLCM) is calculated to determine the energy and the contrast These textural features are used to characterize each gamma modified image and extract text from image. The goal of our work is to study and implement in the HW/SW codesign context the GCM method This method is used for text extraction from a complex color image. The HLS flow is used to design and implement the complex parts of the GCM method as an intellectual property (IPs) block These blocks are connected as hardware coprocessor to the hardcore ARM Cortex-A53 and implemented on Xilinx ZCU102 FPGA board in a HW/SW codesign context to increase the reliability and the efficiency of the GCM method for text extraction.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
