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Expanding Recognizable Distorted Characters Using Self-Corrective Recognition

机译:使用自我纠正识别扩展可识别的扭曲字符

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Large datasets are always demanded for better recognition performance. However, it is not easy to produce them because costly and slow human operators have been necessary for labeling. In the current paper, in order to resolve the problem on yielding large datasets, we propose a scenario for automatic labeling based on the self-corrective recognition algorithm. The strong point of the proposed method is the capability of expanding recognizable distorted characters unlike existing methods. In the experiments, we show a possibility to realize automatic labeling by the method.
机译:始终要求大型数据集更好地识别性能。 然而,生产它们并不容易,因为昂贵和慢的人工操作员都是必要的标签。 在目前的论文中,为了解决屈服大型数据集的问题,我们提出了一种基于自我校正识别算法的自动标记的场景。 与现有方法不同,所提出的方法的强点是扩展可识别的扭曲字符的能力。 在实验中,我们显示了通过该方法实现自动标记的可能性。

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