A new database of Houma Alliance Book ancient handwritten characters and classifier fusion approach
This work addresses the problem of digitizing and recognizing ancient characters for historians and researchers, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the inefficient identification of ancient handwritten characters from the Houma Alliance Book by creating a new database with 297 classes and 3,547 samples and proposing a multi-modal fusion method using deep neural networks, achieving baseline results that demonstrate its efficiency.
The Houma Alliance Book is one of the national treasures of the Museum in Shanxi Museum Town in China. It has great historical significance in researching ancient history. To date, the research on the Houma Alliance Book has been staying in the identification of paper documents, which is inefficient to identify and difficult to display, study and publicize. Therefore, the digitization of the recognized ancient characters of Houma League can effectively improve the efficiency of recognizing ancient characters and provide more reliable technical support and text data. This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters. In the database, 297 classes and 3,547 samples of Houma Alliance ancient handwritten characters are collected from the original book collection and by human imitative writing. Furthermore, the decision-level classifier fusion strategy is applied to fuse three well-known deep neural network architectures for ancient handwritten character recognition. Experiments are performed on our new database. The experimental results first provide the baseline result of the new database to the research community and then demonstrate the efficiency of our proposed method.