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

arXiv: https://arxiv.org/abs/2207.05993 · HTML: https://mlsanity.com/paper/2207.05993/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DenseRAN for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1808.04134/) (2018)
- [Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification](https://mlsanity.com/paper/1309.0261/) (2013)
- [Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1610.04057/) (2016)
- [Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text Kernel](https://mlsanity.com/paper/2107.01547/) (2021)
- [Embedded Large-Scale Handwritten Chinese Character Recognition](https://mlsanity.com/paper/2004.06209/) (2020)

## Abstract (arXiv)

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.