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As the number of digitized historical documents has increased rapidly during the last a few decades, it is necessary to provide efficient methods of information retrieval and knowledge extraction to make the data accessible. Such methods are dependent on optical character recognition (OCR) which converts the document images into textual representations. Nowadays, OCR methods are often not adapted to the historical domain; moreover, they usually need a significant amount of annotated documents. Therefore, this paper introduces a set of methods that allows performing an OCR on historical document images using only a small amount of real, manually annotated training data. The presented complete OCR system includes two main tasks: page layout analysis including text block and line segmentation and OCR. Our segmentation methods are based on fully convolutional networks, and the OCR approach utilizes recurrent neural networks. Both approaches are state of the art in the relevant fields. We have created a novel real dataset for OCR from Porta fontium portal. This corpus is freely available for research, and all proposed methods are evaluated on these data. We show that both the segmentation and OCR tasks are feasible with only a few annotated real data samples. The experiments aim at determining the best way how to achieve good performance with the given small set of data. We also demonstrate that obtained scores are comparable or even better than the scores of several state-of-the-art systems. To sum up, this paper shows a way how to create an efficient OCR system for historical documents with a need for only a little annotated training data.

We present the results of a pilot study concerning the interlaboratory variability of CD34+ enumeration. Three surveys, each including a set of samples, were sent to participating Czech flow cytometry laboratories. The efficacy of this exercise was determined by the reduction in interlaboratory variation and the influence of method used on assay outcome. The variability in results of CD34+ enumeration declined with time. The mean coefficient of variation (CV) of measurement among laboratories dropped, from 58% in first survey to 32% in last survey. All tested variables (gating strategy, platform methodology, sample preparation) affected the variability of the assay. Sample preparation method was associated with a significant bias of absolute CD34+ cell counts. Initially, the outcome of the measurement was also affected by the participating laboratory (identified by a unique laboratory number; ULN). However, laboratories with poorer performance modified their protocols during the study, and the ULN ceased to influence the variability. This study was successful in reducing the interinstitutional variability of CD34+ enumeration. It was shown that the implementation of a standardized protocol does not guarantee accurate measurement. Our research design represents a useful tool, which allows verification of the proper use of a standardized method, the training of operators and feedback in response to the survey results.

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This paper presents an overview of training strategies for optical character recognition of historical documents. The main issue is the lack of the annotated data and its quality. We summarize several ways of synthetic data preparation. The main goal of this paper is to show and compare possibilities how to train a convolutional recurrent neural network classifier using the synthetic data and its combination with a real annotated dataset.

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