@inproceedings{straus_icfhr2018_2018,
	title = {{ICFHR2018} {Competition} on {Automated} {Text} {Recognition} on a {READ} {Dataset}},
	doi = {10.1109/ICFHR-2018.2018.00089},
	abstract = {We summarize the results of a competition on Automated Text Recognition targeting the effective adaptation of recognition engines to essentially new data. The task consists in achieving a minimum character error rate on a previously unknown text corpus from which only a few pages are available for adjusting an already pre-trained recognition engine. This issue addresses a frequent application scenario where only a small amount of task-specific training data is available, because producing this data usually requires much effort. We present the results of five submission. They show that the task is a challenging issue but for certain documents 16 pages of transcription are sufficient to adapt a pre-trained recognition system.},
	booktitle = {2018 16th {International} {Conference} on {Frontiers} in {Handwriting} {Recognition} ({ICFHR})},
	author = {Strauß, Tobias and Leifert, Gundram and Labahn, Roger and Hodel, Tobias and Mühlberger, Günter},
	month = aug,
	year = {2018},
	keywords = {Computational modeling, Data models, Optical imaging, Task analysis, Text recognition, Training, Training data, automated text recognition, fast adaptation, few shot learning, historical documents},
	pages = {477--482},
}

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