Toward Automatic Identification of Dataset Names in Scholarly Articles As the number of scholarly articles we can access is increasing, it becomes possible to read them freely. However, it is difficult to understand scholarly articles since they are basically written for experts. Our big goal is, developing methods to extract essential elements of articles, to facilitate open innovation. To this end, this paper is devoted to considering automatic identification of dataset names in articles. Because a dictionary of datasets is necessary for evaluation, existing methods have focused on some specific discipline. To achieve applicability to any disciplines, we adopt a machine learning approach with a huge amount of scholarly papers. Because we treat papers in multidisciplines, it is challenging how to evaluate experimental results. To solve it, we quantitatively evaluate experimental results with precisionN, which does not require to know all the dataset names in the papers we use, and qualitatively check if candidate tokens are dataset names or not using a GUI tool we have developed. While about 13 tokens of the top 20 output by our method were dataset names, the other ones are names of methods, models, or organizations. So it is important future work to remove such noise results, using additive compositionality of word vectors.

The Equipment Nameplate Dataset for Scene Text Detection and Recognition In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intraclass inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly available on the website.