论文标题
Ubicomp Digital 2020-使用卷积重复网络的手写分类
Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network
论文作者
论文摘要
ubicomp Digital 2020-来自Stabilo的时间序列分类挑战是多变量时间序列分类的挑战。从100位志愿者作家那里收集的数据，并包含15个用笔上的多个传感器测量的功能。在本文中，我们使用神经网络将数据分类为52个类，即阿拉伯语字母的下部和上部案例。神经网络A的拟议架构是CNN-LSTM网络。它将短期上下文的卷积神经网络（CNN）与沿短期存储层（LSTM）结合在一起，以便长期依赖性。我们的作家独家测试集的精度为68％，在盲挑战测试集中达到64.6％，导致第二名。
The Ubicomp Digital 2020 -- Time Series Classification Challenge from STABILO is a challenge about multi-variate time series classification. The data collected from 100 volunteer writers, and contains 15 features measured with multiple sensors on a pen. In this paper,we use a neural network to classify the data into 52 classes, that is lower and upper cases of Arabic letters. The proposed architecture of the neural network a is CNN-LSTM network. It combines convolutional neural network (CNN) for short term context with along short term memory layer (LSTM) for also long term dependencies. We reached an accuracy of 68% on our writer exclusive test set and64.6% on the blind challenge test set resulting in the second place.
