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.

HITHCD2018: Handwritten Chinese Character Database of 21KCategory Current state of handwritten Chinese character recognition (HCCR) conducted on wellconfined character set, far from meeting industrial requirements. The paper describes the creation of a largescale handwritten Chinese character database. Constructing the database is an effort to scale up Chinese handwritten character classification task to cover the full list of GBK character set specification. It consists of 21thousand Chinese character categories and 20million character images, larger than previous databases both in scale and diversity. We present solutions to the challenges of collecting and annotating such largescale handwritten character samples. We elaborately design the sampling strategy, extract salient signals in a systematic way, annotate the tremendous characters through three distinct stages. Experiments are conducted the generalization to other handwritten character databases and our database demonstrates great values. Surely, its scale opens unprecedented opportunities both in evaluation of character recognition algorithms and in developing new techniques.