State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
复制标题
使用开源引擎对 19 世纪 Fraktur 脚本进行最先进的光学字符识别
DOI：
10.5281/zenodo.4622015
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
F. Puppe
中科院分区：
文献类型：
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作者：
Christian Reul;U. Springmann;C. Wick;F. Puppe
在本文中，我们评估光学字符识别（OCR）的19世纪世纪的Fraktur脚本使用混合模型，即模型训练，以识别各种字体和排版从以前看不见的来源。我们描述了强大的混合OCR模型的训练过程，并将其与流行的开源引擎OCRopus和Tesseract以及最先进的商业系统ABBYY的免费模型进行比较。为了进行评估，我们使用了从书籍、期刊和19世纪世纪的字典中收集的各种看不见的数据。实验表明，用真实的数据训练混合模型比用合成数据训练要好上级，并且新型OCR引擎Calamari的性能大大优于其他引擎，平均将ABBYY的字符错误率（CER）降低了70%以上，导致平均CER低于1%。
In this paper we evaluate Optical Character Recognition (OCR) of 19th century Fraktur scripts without book-specific training using mixed models, i.e. models trained to recognize a variety of fonts and typesets from previously unseen sources. We describe the training process leading to strong mixed OCR models and compare them to freely available models of the popular open source engines OCRopus and Tesseract as well as the commercial state of the art system ABBYY. For evaluation, we use a varied collection of unseen data from books, journals, and a dictionary from the 19th century. The experiments show that training mixed models with real data is superior to training with synthetic data and that the novel OCR engine Calamari outperforms the other engines considerably, on average reducing ABBYYs character error rate (CER) by over 70%, resulting in an average CER below 1%.