Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements In this paper, we conduct a largescale study of font statistics in book covers and online advertisements. Through the statistical study, we try to understand how graphic designers relate fonts and content genres and identify the relationship between font styles, colors, and genres. We propose an automatic approach to extract font information from graphic designs by applying a sequence of character detection, style classification, and clustering techniques to the graphic designs. The extracted font information is accumulated together with genre information, such as romance or business, for further trend analysis. Through our unique empirical study, we show that the collected font statistics reveal interesting trends in terms of how typographic design represents the impression and the atmosphere of the content genres.

ICDAR2019 Competition on Historical Book Analysis  HBA2019 In this paper, we present an evaluative study of pixellabeling methods using the HBA 1.0 dataset for historical book analysis. This study is held in the context of the 2nd historical book analysis (HBA2019) competition and in conjunction with the 15th IAPR international conference on document analysis and recognition (ICDAR2019). The HBA2019 competition provides a large experimental corpus and a thorough evaluation protocol to ensure an objective performance benchmarking of pixellabeling document image methods. Two nested challenges are evaluated in the HBA2019 competition: Challenge 1 and Challenge 2. Challenge 1 evaluates how image analysis methods could discriminate the textual content from the graphical ones at pixel level. Challenge 2 assesses the capabilities of pixellabeling methods to separate the textual content according to different text fonts (e.g. lowercase, uppercase, italic, etc.) at pixel level. During the competition, we received 52 and 38 different teamsu0027 registrations for Challenge 1 and Challenge 2, respectively and finally 5 of them submitted their results in each challenge. Qualitative and numerical results of the participating methods in both challenges are reported and discussed in this paper in order to provide a baseline for future evaluation studies in historical document image analysis. The evaluation shows that the method submitted by the NLPRCASIA team achieves the highest performance in both challenges.