Word Spotting and Recognition with Embedded Attributes
Word Spotting and Recognition with Embedded Attributes
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DOI：
10.1109/tpami.2014.2339814
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发表时间：
2014-12-01
影响因子：
23.6
通讯作者：
Valveny, Ernest
中科院分区：
文献类型：
--
作者：
Almazan, Jon;Gordo, Albert;Valveny, Ernest
本文解决了图像上的单词识别和单词识别问题。在单词识别中，目标是在图像数据集中找到查询单词的所有实例。在识别中，目标是识别单词图像的内容，通常借助字典或词典。我们描述了一种将单词图像和文本字符串嵌入到公共向量子空间中的方法。这是通过标签嵌入和属性学习以及公共子空间回归的组合来实现的。在这个子空间中，表示同一单词的图像和字符串靠近在一起，允许将识别和检索任务视为最近邻问题。与大多数其他现有方法相反，我们的表示具有固定长度、低维，并且计算速度非常快，尤其是比较速度非常快。我们在手写文档和自然图像的四个公共数据集上测试了我们的方法，显示结果与发现和识别任务的最新技术相当或更好。
This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.