I s OBS : An Information System for Oracle Bone Script (2020)

Abstract: Oracle bone script (OBS) is the earliest known ancient Chinese writing system and the ancestor of modern Chinese. As the Chinese writing system is the oldest continuously-used system in the world, the study of OBS plays an important role in both linguistic and historical research. In order to utilize advanced machine learning methods to automatically process OBS, we construct an information system for OBS (IsOBS) to symbolize, serialize, and store OBS data at the character-level, based on efficient databases and retrieval modules. Moreover, we also apply few-shot learning methods to build an effective OBS character recognition module, which can recognize a large number of OBS characters (especially those characters with a handful of examples) and make the system easy to use. The demo system of IsOBS can be found from http://isobs.thunlp.org/ . In the future, we will add more OBS data to the system, and hopefully our IsOBS can support further efforts in automatically processing OBS and advance the scientific progress in this field.

## introduction oracle bone script (obs) refers to characters carved on animal bones or turtle plastrons. to research obs is important for both chinese linguistic and historical research: (1) as shown in figure 1 , obs is the direct ancestor of modern chinese and closely related to other languages in east asia @xcite . analysis and understanding of obs is vital for studying the etymology and historical evolution of chinese as well as other east asian languages. (2) as shown in figure 2 , on one obs document carved on one animal bone or turtle plastron, the number of characters ranges from fewer than ten to more than one hundred. besides, as obs is used for divination in ancient china, these documents cover a variety of topics, including war, ceremonial sacrifice, agriculture, as well as births, illnesses, and deaths of royal members @xcite . therefore, obs documents constitute the earliest chinese textual corpora, and to analyze and understand obs is of great significance to historical research. considering that it is often sophisticated and time-consuming to manually process ancient languages, some efforts have been devoted to utilizing machine learning techniques in this field. in order to detect and recognize ancient characters, anderson and @xcite ; @xcite ; @xcite ; @xcite ; @xcite utilize computer vision techniques to visualize cuneiform tablets and recognize cuneiform characters, @xcite ; @xcite ; @xcite apply similar techniques to recognize egyptian hieroglyphs. for understanding the ancient text, @xcite first show the feasibility of automatically deciphering a dead language by designing a bayesian model to match the alphabet with non-parallel data. then, @xcite propose a more effective decipherment approach and achieve promising results. @xcite adopt a method similar to non-parallel machine translation @xcite to decipher related languages, which further inspires @xcite to propose a novel neural approach for automatic decipherment of ugaritic and linear b. @xcite ; @xcite explore to learn language models for cuneiform text. these previous efforts have inspired us to apply machine learning methods to the task of processing obs. however, there are still three main challenges: (1) different from those ancient greek and central asian scripts, in which letters are mainly used to constitute words and sentences, obs is hieroglyphic and does not have any delimiter to mark word boundaries. this challenge also exists in modern chinese scenarios. (2) although obs is the origin of modern chinese, it is quite different from modern chinese characters. typically, one obs character may have different glyphs. moreover, there are many compound obs characters corresponding to multiple modern chinese words. (3) there still lacks an effective and stable system to symbolize and serialize obs data. most obs data is stored in the form of unserialized bone/plastron photos, which cannot support either recognizing characters or understanding text. the above three challenges make it difficult to use existing machine learning methods for understanding obs, and the third one is the most crucial. to this end, we construct an information system for obs (isobs) to symbolize and serialize obs data at the character-level, so that we can utilize machine learning methods to process obs in the fu-ture: (1) we construct an obs character database, where each character is matched to corresponding modern chinese character (if it has been deciphered) and incorporates a variety of its glyphs. (2) we construct an obs document database, which stores more than 5, 000 obs documents. we also split the images of these documents into character images, and use these character images to construct both the obs and corresponding modern chinese character sequences for each document. (3) we also implement a character recognition module for obs characters based on few-shot learning models, considering there are only a handful of examples for each obs character. based on the character recognition module, we construct an information retrieval module for searching in character and document databases. the databases, character recognition module, and retrieval module of isobs provide an effective and efficient approach to symbolize, serialize, and store the data of obs. we believe isobs can serve as a footstone to support further research (especially character recognition and language understranding) on automatically processing obs in the future. ## application scenarios as mentioned before, isobs is designed for symbolizing, serializing, and storing the obs data. hence, the application scenarios of isobs mainly focus on constructing databases for both obs characters and documents, as well as implementing character recognition and retrieval modules for data search. ## system framework and details in this section, we mainly focus on introducing the overall framework and details of our system, especially introducing how to construct obs databases and build the character recognition module. the overall framework of isobs including all databases and modules is shown in figure 6 . ## experiment and evaluation we evaluate different character recognition models on self-created dataset. the results show that our implementation of prototypical network can achieve stable and competitive results. the datasets and source code can be found from https: //github.com/thunlp/isobs. ## conclusion and future work as to research obs is important for both chinese linguistic and historical research, we thus construct an information system for obs and name the system isobs. isobs provides an open digitalized platform consisting of the obs databases, the character recognition module, and the retrieval module. the experimental results further demonstrate that our character recognition module based on fewshot learning models have achieved satisfactory performance on our self-created hand-written obs character dataset. in the future, we plan to explore the following directions: (1) to include more obs document and character data from collection books into our existing databases, (2) to employ generative learning and adversarial algorithms to add more robustness to our model, and (3) to construct a language model for ancient languages. we believe that these three directions will be beneficial for ancient languages research and support further exploration of utilizing machine learning for understanding obs.