Bag-of-Features HMMs for Segmentation-Free Word Spotting in Handwritten Documents
Bag-of-Features HMMs for Segmentation-Free Word Spotting in Handwritten Documents
复制标题
用于手写文档中无分段单词识别的特征包 HMM
DOI：
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发表时间：
2013
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通讯作者：
G. Fink
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作者：
Leonard Rothacker;Marçal Rusiñol;G. Fink
最近基于 HMM 的手写词识别方法需要大量的学习样本，并且主要依赖于文档的事先分割。我们建议在基于补丁的无分割框架中使用特征袋 HMM，该框架由单个样本估计。特征袋 HMM 使用局部图像特征代表的统计数据。因此，它们可以被视为离散 HMM 的变体，允许对某个时间点的多个特征的观察进行建模。离散性质使我们能够仅使用用户提供的单个查询示例来估计查询模型。这使得我们的方法在训练数据的可用性方面非常灵活。此外，我们能够超越乔治·华盛顿数据集上最先进的结果。
Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset.