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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Scene text recognition using a Hough forest implicit shape model and semi-Markov conditional random fields
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Scene text recognition using a Hough forest implicit shape model and semi-Markov conditional random fields

机译：使用霍夫森林隐式形状模型和半马尔可夫条件随机场进行场景文本识别

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摘要

Most of the scene text recognition methods utilize character models only in the character recognition phase, the last stage of the process. In former phases such as text detection, only abstracted features of text regions are used, which might cause loss of information. In this paper, we propose a novel scene text recognition method which fully utilizes model of target characters throughout the process. Each of the target character set is modeled with a part-based object model called implicit shape model (ISM) to achieve robustness for the partial degradation of characters. Towards this end, we trained a Hough forest which localizes and aggregates character parts to detect character candidates in the image. The detected character candidates are verified by organizing the most plausible text lines in a semi-Markov conditional random field (semi-CRF) framework. As concrete character models are utilized throughout the process, even extremely deformed texts are detected and recognized. (C) 2015 Elsevier Ltd. All rights reserved.
机译：大多数场景文本识别方法仅在字符识别阶段（过程的最后阶段）使用字符模型。在以前的阶段（例如文本检测）中，仅使用文本区域的抽象特征，这可能会导致信息丢失。在本文中，我们提出了一种新颖的场景文本识别方法，该方法在整个过程中都充分利用了目标字符模型。每个目标字符集都使用基于零件的对象模型（称为隐式形状模型（ISM））进行建模，以实现部分字符退化的鲁棒性。为此，我们训练了一个霍夫森林，该森林可以定位并聚集角色部分，以检测图像中的候选角色。通过在半马尔可夫条件随机字段（semi-CRF）框架中组织最合理的文本行来验证检测到的候选字符。由于在整个过程中都使用了具体的字符模型，因此甚至可以检测和识别出变形很大的文本。 （C）2015 Elsevier Ltd.保留所有权利。

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