Towards a SignWriting recognition system
Towards a SignWriting recognition system
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迈向手语识别系统
DOI：
10.1109/icdar.2015.7333719
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Britto
中科院分区：
文献类型：
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作者：
D. Stiehl;L. Addams;Luiz Oliveira;C. Guimaraes;A. Britto
SignWriting是一种手语书写系统。它是基于视觉符号来代表手的形状、动作和面部表情，以及其他元素。它已被40多个国家采用，但为了确保聋人社区的社会融合，应将基于手语的书写系统适当地纳入信息技术。本文报告了我们为实现SignWiring自动读取系统所做的第一次努力。这将允许将SignWriting脚本转换为文本，以便能够以有效的方式存储、检索和索引信息。为了使这项工作成为可能，我们一直在收集一个手部结构的数据库，目前它总计有7,994个图像，分为103类符号。为了对这些符号进行分类，我们使用不同的特征、分类器和组合策略进行了一组全面的实验。卷积神经网络的识别率最高，达到94.4%。
SignWriting is a writing system for sign languages. It is based on visual symbols to represent the hand shapes, movements and facial expressions, among other elements. It has been adopted by more than 40 countries, but to ensure the social integration of the deaf community, writing systems based on sign languages should be properly incorporated into the Information Technology. This article reports our first efforts toward the implementation of an automatic reading system for SignWiring. This would allow converting the SignWriting script into text so that one can store, retrieve, and index information in an efficient way. In order to make this work possible, we have been collecting a database of hand configurations, which at the present moment sums up to 7,994 images divided into 103 classes of symbols. To classify such symbols, we have performed a comprehensive set of experiments using different features, classifiers, and combination strategies. The best result, 94.4% of recognition rate, was achieved by a Convolutional Neural Network.