Scene text retrieval aims to localize and search all text instances from an
image gallery, which are the same or similar to a given query text. Such a task
is usually realized by matching a query text to the recognized words, outputted
by an end-to-end scene text spotter. In this paper, we address this problem by
directly learning a cross-modal similarity between a query text and each text
instance from natural images. Specifically, we establish an end-to-end
trainable network, jointly optimizing the procedures of scene text detection
and cross-modal similarity learning. In this way, scene text retrieval can be
simply performed by ranking the detected text instances with the learned
similarity. Experiments on three benchmark datasets demonstrate our method
consistently outperforms the state-of-the-art scene text spotting/retrieval
approaches. In particular, the proposed framework of joint detection and
similarity learning achieves significantly better performance than separated
methods. Code is available at: this https URL

本篇论文提出了一种新的场景文本检索方法，使用交叉模态相似性学习直接匹配查询文本和自然图像中每个文本实例之间的相似性，并通过建立端到端可训练的网络来实现场景文本检测和交叉模态相似性学习的联合优化，在三个基准数据集上的实验表明该方法优于现有的场景文本检测 / 检索方法，尤其是联合检测和相似性学习的框架比分离方法性能更好。

通过联合文本检测和相似性学习实现场景文本检索

Scene Text Retrieval via Joint Text Detection and Similarity Learning

Textual information found in scene images provides high level semantic
information about the image and its context and it can be leveraged for better
scene understanding. In this paper we address the problem of scene text
retrieval: given a text query, the system must return all images containing the
queried text. The novelty of the proposed model consists in the usage of a
single shot CNN architecture that predicts at the same time bounding boxes and
a compact text representation of the words in them. In this way, the text based
image retrieval task can be casted as a simple nearest neighbor search of the
query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase in
processing speed.

这篇论文提出了一种使用单镜头卷积神经网络架构的场景文本检索方法，将基于文本的图像检索任务转换为查询文本表示的最近邻搜索，实现了对大规模图像数据库的快速处理，并在实验中表明该方法优于传统的方法。