复杂彩色图像文本提取的高效硬件/软件设计

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI:10.32604/cmc.2022.024345
Mohamed Amin Ben Atitallah, R. Kachouri, A. Ben Atitallah, H. Mnif
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引用次数: 2

摘要

在构建一个帮助视障人士理解文本的嵌入式系统的背景下,本文提出了一种高效的基于伽玛校正方法(Gamma Correction Method, GCM)的文本提取高级综合(High-level synthesis, HLS)硬件/软件(HW/SW)设计。事实上,GCM是从复杂彩色图像和视频中提取文本的常用方法。本工作的目的是研究GCM方法在Xilinx ZCU102 FPGA板上的复杂性,并在考虑文本提取质量的情况下,利用HLS流提出了一种作为该方法中关键块的知识产权(IP)块的硬件实现。该IP集成并连接到ARM Cortex-A53作为硬件/软件协同设计环境中的协处理器。实验结果表明,GCM方法在ZCU102 FPGA板上的HLS硬件/软件实现与软件实现相比,处理时间减少了约89%。这个结果是基于文本提取的软件实现的相同效力和强度给出的。
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An Efficient HW/SW Design for Text Extraction from Complex Color Image
: 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.
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来源期刊
Cmc-computers Materials & Continua
Cmc-computers Materials & Continua 工程技术-材料科学:综合
CiteScore
5.30
自引率
19.40%
发文量
345
审稿时长
1 months
期刊介绍: This journal publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials. Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.
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