This article discusses the authors’ approach in creating a digital library which can be used to access ancient historical documents written in traditional

This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.

Collector's seals provide important clues about the ownership of a book. They contain much information pertaining to the essential elements of ancient materials and also show the details of possession, its relation to the book, the identity of the collectors and their social status and wealth, amongst others. Asian collectors have typically used artistic ancient characters rather than modern ones to make their seals. In addition to the owner's name, several other words are used to express more profound meanings. A system that automatically recognizes these characters can help enthusiasts and professionals better understand the background information of these seals. However, there is a lack of training data and labelled images, as samples of some seals are scarce and most of them are degraded images. It is necessary to find new ways to make full use of such scarce data. While these data are available online, they do not contain information on the characters' position. The goal of this research is to assist in obtaining more labelled data through user interaction and provide retrieval tools that use only standard character typefaces extracted from font files. In this paper, a character segmentation method is proposed to predict the candidate characters' area without any labelled training data that contain character coordinate information. A retrieval-based recognition system that focuses on a single character is also proposed to support seal retrieval and matching. The experimental results demonstrate that the proposed character segmentation method performs well on Asian collector's seals, with 85% of the test data being correctly segmented.

This research article provides a summary of our approach in providing integrated multilingual access to diverse digital archives of Japanese Ukiyo-e prints. We propose a novel method, which generates links across databases using Linked Data. A retrieval system has been developed for realizing the proposed method, which dynamically creates links to miscellaneous Linked Data resources from authority data. By using the proposed method, users would allowed to access to additional data about a certain record in multiple databases not depending on languages and formats of each database. Experimental evaluations have conducted to measure reliability of the generated links. The experimental results show high accuracy rate and prove the effectiveness of the proposed method. Links to major Linked Data resources were created successfully.

In this poster, we demonstrate a named entity extraction method for digitized ancient Mongolian documents by using the features of characters' appearance in a word. Named entities such as personal names and place names will be extracted by employing Support Vector Machine that aims to reduce the labor-intensive analysis on historical text. The preliminary results of our experiment show that the proposed method has gained 0.6993, 0.5679 and 0.6268 of precision, recall and F-measure, respectively.