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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Real-Time Lexicon-Free Scene Text Localization and Recognition
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Real-Time Lexicon-Free Scene Text Localization and Recognition

机译：实时无词典场景文本本地化和识别

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摘要

An end-to-end real-time text localization and recognition method is presented. Its real-time performance is achieved by posing the character detection and segmentation problem as an efficient sequential selection from the set of Extremal Regions. The ER detector is robust against blur, low contrast and illumination, color and texture variation. In the first stage, the probability of each ER being a character is estimated using features calculated by a novel algorithm in constant time and only ERs with locally maximal probability are selected for the second stage, where the classification accuracy is improved using computationally more expensive features. A highly efficient clustering algorithm then groups ERs into text lines and an OCR classifier trained on synthetic fonts is exploited to label character regions. The most probable character sequence is selected in the last stage when the context of each character is known. The method was evaluated on three public datasets. On the ICDAR 2013 dataset the method achieves state-of-the-art results in text localization; on the more challenging SVT dataset, the proposed method significantly outperforms the state-of-the-art methods and demonstrates that the proposed pipeline can incorporate additional prior knowledge about the detected text. The proposed method was exploited as the baseline in the ICDAR 2015 Robust Reading competition, where it compares favourably to the state-of-the art.
机译：提出了一种端到端的实时文本定位与识别方法。它的实时性能是通过将字符检测和分割问题视为从极值区域集中进行的有效顺序选择来实现的。 ER检测器可抵抗模糊，低对比度和照明，颜色和纹理变化。在第一阶段，使用新颖算法在恒定时间内计算出的特征来估计每个ER为字符的概率，而在第二阶段中仅选择局部概率最大的ER，在此阶段，使用计算上更昂贵的特征可以提高分类精度。然后，一种高效的聚类算法将ER分组为文本行，并利用在合成字体上训练的OCR分类器来标记字符区域。当知道每个字符的上下文时，在最后阶段选择最可能的字符序列。该方法在三个公共数据集上进行了评估。在ICDAR 2013数据集上，该方法可实现文本本地化的最新结果；在更具挑战性的SVT数据集上，提出的方法明显优于最新方法，并证明提出的管道可以结合有关检测到的文本的其他先验知识。拟议的方法被用作ICDAR 2015年稳健阅读竞赛的基准，在该技术中，该方法可与最新技术相媲美。

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