Collecting Handwritten Nom Character Patterns from Historical Document Pages

In this paper, we present methods of segmenting Nom historical documents and clustering character patterns to build a Nom character pattern database. Nom is an ideographic script to represent Vietnamese, used from the 10th century to 20th century. However, this heritage is nearly lost. In order to preserve the wisdom and knowledge expressed in Nom, recognition and digitalization are indispensable. Because there is no OCR for Nom yet, we have to start from collecting patterns. We have employed a projection profile based method for segmenting hundreds of pages into individual characters. Then, we have implemented a combination of Chinese OCR-based clustering and K-means clustering to group characters into categories. The experiment shows that the proposed system can help collecting the characters patterns effectively. Moreover, it has revealed that there are many character classes lost or uncategorized so far.