Cultural Psychology Analysis on Ancient Chinese Archive With the developing of big data analysis technology and the digitization of ancient Chinese books, we are able to do more thoroughly psychologyrelated analysis on Chinese history, culture and people. It is an opportunity that we can have a glance at Chinese behavioral and psychological variation from 2000 years ago to the beginning of the nineteenth century. We aim at figuring out the change of the mental process of Chinese people through the past 2000 years and how the mind and behavior are shaped by the specific culture at each stage, based on the analysis of behavior and psychology, with the usage of big data and artificial intelligence techniques. In this paper, we construct the Ancient Chinese Archive (ACA), beginning with twentyfive official dynastic history books. The timeline begins with the preQin period and ends with Qing dynastic. Our selection contains about 27 million Chinese characters (except punctuations). For better observation of the variation tendencies through every period, we merge some books into one period and divide all the dynasties into 9 parts. In each period, we count the frequency of some keywords such as Xiao (xe5xadx9d, filial piety), Lxc7x90 (xe7xa6xaexe7xa4xbc, proper rite), etc., which represent Chinese social morality and culture ethics. We analyze the change process of the word frequency through time and areas and try to find out the explanation of these trends.

CASIAAHCDB: A LargeScale Chinese Ancient Handwritten Characters Database This paper introduces a Chinese Ancient Handwritten Characters Database (CASIAAHCDB) for character recognition research. The database was built by annotating 11,937 pages of Chinese ancient handwritten documents. It consists of more than 2.2 million annotated handwritten character samples of 10,350 categories. According to the source of these documents, the database is divided into two datasets of different styles: Complete Library in Four Sections (AHCDBstyle1) and Ancient Buddhist Scriptures (AHCDBstyle2). Each dataset can be divided into three parts based on its applications. The first part, called basic category set, contains samples of common categories in two datasets, and is suitable for basic character recognition task. The second part, called enhanced category set, is mainly used for openset character recognition task based on the basic character recognition. The third part, called the reserved category set, can be used in many pattern recognition tasks in the future. Based on the large category set, the various writing styles and the imbalanced sample number per category, CASIAAHCDB can also be used for various classification and learning tasks such as transfer learning, fewshot learning. We performed experiments of basic character recognition on the basic category set, and report the results for benchmark. More techniques can be evaluated on this challenging database in the future.