Segmentation of Arabic manuscripts into lines of text and words is an
important step to make recognition systems more efficient and accurate. The
problem of segmentation into text lines is solved since there are carefully
annotated dataset dedicated to this task. However, To the best of our
knowledge, there are no dataset annotating the word position of Arabic texts.
In this paper, we present a new dataset specifically designed for historical
Arabic script in which we annotate position in word level.

将阿拉伯手稿分割成文本行和单词是使识别系统更高效准确的重要步骤。我们提出了一个新的数据集，专门设计用于历史阿拉伯脚本，并在单词层面进行位置标注。

阿拉伯手写文本行数据集

Arabic Handwritten Text Line Dataset

Recently there has been a dramatic increase in the performance of recognition
systems due to the introduction of deep architectures for representation
learning and classification. However, the mathematical reasons for this success
remain elusive. This tutorial will review recent work that aims to provide a
mathematical justification for several properties of deep networks, such as
global optimality, geometric stability, and invariance of the learned
representations.

本文综述了近期深度神经网络在识别系统方面表现出的很高的性能，以及为数不多的数学解释其成功的理由，如全局最优性、几何稳定性和表示学习的不变性。

深度学习数学

Mathematics of Deep Learning

We introduce algorithms to visualize feature spaces used by object detectors.
Our method works by inverting a visual feature back to multiple natural images.
We found that these visualizations allow us to analyze object detection systems
in new ways and gain new insight into the detector's failures. For example,
when we visualize the features for high scoring false alarms, we discovered
that, although they are clearly wrong in image space, they do look deceptively
similar to true positives in feature space. This result suggests that many of
these false alarms are caused by our choice of feature space, and supports that
creating a better learning algorithm or building bigger datasets is unlikely to
correct these errors. By visualizing feature spaces, we can gain a more
intuitive understanding of recognition systems.

介绍了一种对物体探测器使用的特征空间进行可视化的算法，通过将视觉特征还原为多个自然图像来工作，并通过对高分假警报的特征进行可视化分析，探索了错误警报产生的原因并提出了矫正方法。

可视化物体检测特征

Visualizing Object Detection Features

In this paper we present and start analyzing the iCub World data-set, an
object recognition data-set, we acquired using a Human-Robot Interaction (HRI)
scheme and the iCub humanoid robot platform. Our set up allows for rapid
acquisition and annotation of data with corresponding ground truth. While more
constrained in its scopes -- the iCub world is essentially a robotics research
lab -- we demonstrate how the proposed data-set poses challenges to current
recognition systems. The iCubWorld data-set is publicly available. The data-set
can be downloaded from: this http URL

通过以 iCub 机器人为平台、透过人机互动方式获取数据、并提供真实数据的验证基准，我们呈现并分析了 iCub World 数据集。虽然在一个受限的环境（如机器人研究实验室）中实施，我们演示了如何通过该数据集挑战当前的识别系统。