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		<doi>10.1109/SIBGRAPI51738.2020.00037</doi>
		<citationkey>ZeniJung:2020:WeSuCh</citationkey>
		<title>Weakly Supervised Character Detection for License Plate Recognition</title>
		<format>On-line</format>
		<year>2020</year>
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		<size>885 KiB</size>
		<author>Zeni, Luis Felipe,</author>
		<author>Jung, Claudio,</author>
		<affiliation>Universidade Federal do Rio Grande do Sul</affiliation>
		<affiliation>Universidade Federal do Rio Grande do Sul</affiliation>
		<editor>Musse, Soraia Raupp,</editor>
		<editor>Cesar Junior, Roberto Marcondes,</editor>
		<editor>Pelechano, Nuria,</editor>
		<editor>Wang, Zhangyang (Atlas),</editor>
		<e-mailaddress>luis.zeni@inf.ufrgs.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)</conferencename>
		<conferencelocation>Porto de Galinhas (virtual)</conferencelocation>
		<date>7-10 Nov. 2020</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>weakly supervised character detection, licence plate recognition, character detection, neural networks.</keywords>
		<abstract>Automatic Licence Plate Recognition (ALPR) is an essential task in the context of intelligent transportation systems. In a typical ALPR pipeline, the last stage receives as input a cropped license plate region and outputs the string with the plate characters. This paper presents a Weakly Supervised Character Detection (WSCD) approach that requires only string-level annotations (as in generic text recognition methods) but is able to detect characters individually (as in detection-based methods, which require character-level annotations). The proposed method is evaluated in five distinct datasets and present very competitive results against other state-of-the-art methods.</abstract>
		<language>en</language>
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