Care Label Recognition

Jiri Kralicek, Jiri Matas, M. Busta
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引用次数: 1

Abstract

The paper introduces the problem of care label recognition and presents a method addressing it. A care label, also called a care tag, is a small piece of cloth or paper attached to a garment providing instructions for its maintenance and information about e.g. the material and size. The informationand instructions are written as symbols or plain text. Care label recognition is a challenging text and pictogram recognition problem - the often sewn text is small, looking as if printed using a non-standard font; the contrast of the text gradually fades, making OCR progressively more difficult. On the other hand, the information provided is typically redundant and thus it facilitates semi-supervised learning. The presented care label recognition method is based on the recently published End-to-End Method for Multi-LanguageScene Text, E2E-MLT, Busta et al. 2018, exploiting specific constraints, e.g. a care label vocabulary with multi-language equivalences. Experiments conducted on a newly-created dataset of 63 care label images show that even when exploiting problem-specific constraints, a state-of-the-art scene text detection and recognition method achieve precision and recall slightly above 0.6, confirming the challenging nature of the problem.
护理标签识别
介绍了护理标签识别问题,并提出了一种解决该问题的方法。护理标签,也叫护理标签,是贴在衣服上的一小块布或纸,提供保养说明和诸如材料和尺寸等信息。信息和说明以符号或纯文本的形式书写。护理标签识别是一个具有挑战性的文本和象形文字识别问题-通常缝制的文本很小,看起来好像使用非标准字体打印;文本的对比度逐渐减弱,使OCR逐渐变得更加困难。另一方面,所提供的信息通常是冗余的,因此它有利于半监督学习。提出的护理标签识别方法基于最近发表的多语言场景文本的端到端方法,E2E-MLT, Busta等人。2018,利用特定的约束,例如具有多语言等价的护理标签词汇表。在新创建的63张护理标签图像数据集上进行的实验表明,即使在利用特定于问题的约束条件时,最先进的场景文本检测和识别方法的精度和召回率也略高于0.6,这证实了问题的挑战性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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