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		<identifier>8JMKD3MGPEW34M/4ACGJTH</identifier>
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		<citationkey>NascimentoLaroMeno:2023:SuToLi</citationkey>
		<title>Super-Resolution Towards License Plate Recognition</title>
		<format>On-line</format>
		<year>2023</year>
		<numberoffiles>1</numberoffiles>
		<size>808 KiB</size>
		<author>Nascimento, Valfride,</author>
		<author>Laroca, Rayson,</author>
		<author>Menotti, David,</author>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<editor>Clua, Esteban Walter Gonzalez,</editor>
		<editor>Körting, Thales Sehn,</editor>
		<editor>Paulovich, Fernando Vieira,</editor>
		<editor>Feris, Rogerio,</editor>
		<e-mailaddress>vwnascimento@inf.ufpr.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio Grande, RS</conferencelocation>
		<date>Nov. 06-09, 2023</date>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>PixelShuffle, Reconstruction, Super-Resolution.</keywords>
		<abstract>Recent years have seen significant developments in license plate recognition through the integration of deep learning techniques and the increasing availability of training data. Nevertheless, reconstructing license plates from low-resolution surveillance footage remains challenging. To address this issue, we propose an attention-based super-resolution approach that incorporates sub-pixel convolution layers and an Optical Character Recognition (OCR)-based loss function. We trained the proposed architecture on synthetic images created by applying heavy Gaussian noise followed by bicubic downsampling to high-resolution license plate images. Our results show that the proposed approach for reconstructing these low-resolution images substantially outperforms existing methods in both quantitative and qualitative measures. Our source code is publicly available at https://github.com/valfride/lpr-rsr-ext/.</abstract>
		<language>en</language>
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