Automatic sign language recognition is a research area that encompasses
human-computer interaction, computer vision and machine learning. Robust
automatic recognition of sign language could assist in the translation process
and the integration of hearing-impaired people, as well as the teaching of sign
language to the hearing population. Sign languages differ significantly in
different countries and even regions, and their syntax and semantics are
different as well from those of written languages. While the techniques for
automatic sign language recognition are mostly the same for different
languages, training a recognition system for a new language requires having an
entire dataset for that language. This paper presents a dataset of 64 signs
from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains
3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first
step towards building a comprehensive research-level dataset of Argentinian
signs, specifically tailored to sign language recognition or other machine
learning tasks. The subjects that performed the signs wore colored gloves to
ease the hand tracking and segmentation steps, allowing experiments on the
dataset to focus specifically on the recognition of signs. We also present a
pre-processed version of the dataset, from which we computed statistics of
movement, position and handshape of the signs.

自动手语识别是一个研究领域，包括人机交互、计算机视觉和机器学习。该论文介绍了一个涉及阿根廷手语的数据集，名为 LSA64，旨在构建一个针对阿根廷手语识别或其他机器学习任务的全面研究级数据集的第一步。

LSA64: 阿根廷手语数据集

LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is an important topic within the areas of
human-computer interaction and machine learning. On the one hand, it poses a
complex challenge that requires the intervention of various knowledge areas,
such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people.
This paper offers two main contributions: first, the creation of a database
of handshapes for the Argentinian Sign Language (LSA), which is a topic that
has barely been discussed so far. Secondly, a technique for image processing,
descriptor extraction and subsequent handshape classification using a
supervised adaptation of self-organizing maps that is called ProbSom. This
technique is compared to others in the state of the art, such as Support Vector
Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA handshapes, and
is a first step towards building a comprehensive database of Argentinian signs.
The ProbSom-based neural classifier, using the proposed descriptor, achieved an
accuracy rate above 90%.

自动手语识别是人机交互和机器学习领域的重要课题之一，本文的主要贡献是创建了阿根廷手语的手指形状数据库，并提出了一种基于图像处理和 ProbSom 的手指形状分类技术，实验结果表明该技术的精确率超过 90%。

利用 ProbSom 识别阿根廷手语的手势形状

Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition (SLR) is an important topic within the
areas of human-computer interaction and machine learning. On the one hand, it
poses a complex challenge that requires the intervention of various knowledge
areas, such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people, as well as the teaching of sign language for the hearing population.
SLR systems usually employ Hidden Markov Models, Dynamic Time Warping or
similar models to recognize signs. Such techniques exploit the sequential
ordering of frames to reduce the number of hypothesis. This paper presents a
general probabilistic model for sign classification that combines
sub-classifiers based on different types of features such as position, movement
and handshape. The model employs a bag-of-words approach in all classification
steps, to explore the hypothesis that ordering is not essential for
recognition. The proposed model achieved an accuracy rate of 97% on an
Argentinian Sign Language dataset containing 64 classes of signs and 3200
samples, providing some evidence that indeed recognition without ordering is
possible.

该研究探讨了自动手语识别的问题，提出了一种基于概率模型的手语分类方法，通过对具有 64 个类别和 3200 个样本的阿根廷手语数据集进行测试，证实了可以实现不考虑顺序的识别，并取得了 97% 的准确率。