A new database of Houma Alliance Book ancient handwritten characters and classifier fusion approach

TL;DR

This paper introduces a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion recognition method, significantly improving recognition efficiency and providing a valuable resource for historical research.

Contribution

The paper presents a novel database of 297 classes and 3,547 samples of ancient characters and a classifier fusion approach for improved recognition accuracy.

Findings

The database provides a new resource for research.

The fusion method achieves better recognition performance.

Baseline results establish a benchmark for future studies.

Abstract

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.