This paper addresses the problem of learning word image representations:
given the cropped image of a word, we are interested in finding a descriptive,
robust, and compact fixed-length representation. Machine learning techniques
can then be supplied with these representations to produce models useful for
word retrieval or recognition tasks. Although many works have focused on the
machine learning aspect once a global representation has been produced, little
work has been devoted to the construction of those base image representations:
most works use standard coding and aggregation techniques directly on top of
standard computer vision features such as SIFT or HOG.
We propose to learn local mid-level features suitable for building word image
representations. These features are learnt by leveraging character bounding box
annotations on a small set of training images. However, contrary to other
approaches that use character bounding box information, our approach does not
rely on detecting the individual characters explicitly at testing time. Our
local mid-level features can then be aggregated to produce a global word image
signature. When pairing these features with the recent word attributes
framework of Almaz\'an et al., we obtain results comparable with or better than
the state-of-the-art on matching and recognition tasks using global descriptors
of only 96 dimensions.

本论文研究了对单词图像进行描述性、稳定性和紧凑性表示的问题，提出了一种基于学习本地中层特征的方法，并与最新的单词属性框架相结合，得到了可与全球描述符相媲美的结果。