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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Validation of image defect models for optical character recognition
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Validation of image defect models for optical character recognition

机译：验证用于光学字符识别的图像缺陷模型

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Considers the problem of evaluating character image generators that model distortions encountered in optical character recognition (OCR). While a number of such defect models have been proposed, the contention that they produce the desired result is typically argued in an ad hoc and informal way. The authors introduce a rigorous and more pragmatic definition of when a model is accurate: they say a defect model is validated if the OCR errors induced by the model are indistinguishable from the errors encountered when using real scanned documents. The authors describe four measures to quantify this similarity, and compare and contrast them using over ten million scanned and synthesized characters in three fonts. The measures differentiate effectively between different fonts and different scans of the same font regardless of the underlying text.
机译：考虑评估字符图像生成器的问题，该图像生成器对光学字符识别（OCR）中遇到的失真进行建模。虽然已经提出了许多这样的缺陷模型，但是通常以临时性和非正式的方式争论它们产生期望结果的争论。作者介绍了模型何时准确的严格和更实际的定义：他们说，如果模型引起的OCR错误与使用真实扫描文档时遇到的错误没有区别，则可以验证缺陷模型。作者描述了四种量化这种相似性的方法，并使用超过一千万种扫描和合成的三种字体的字符对它们进行了比较和对比。这些措施可以有效地区分不同的字体和相同字体的不同扫描，而与基础文本无关。

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