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Related Experiment Video

Updated: Oct 13, 2025

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

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FAN-MCCD: Fast and Accurate Network for Multi-Scale Chinese Character Detection.

Manar Alnaasan¹, Sungho Kim¹

¹Department of Electronics Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan-si 38541, Korea.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This summary is machine-generated.

This study introduces an efficient method for accurately detecting handwritten Chinese characters in historical documents, significantly improving detection rates and reducing errors compared to existing models.

Keywords:

handwritten in old documents multi-scale Chinese character detection multiscale feature network simple pipeline

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Area of Science:

Computer Vision
Document Analysis
Artificial Intelligence

Background:

Scale variation in character detection leads to inaccurate localization, particularly in historical and handwritten documents.
Overconfidence in document analysis results is a significant issue within the research community.

Purpose of the Study:

To explore the performance of a state-of-the-art network for fast and accurate handwritten Chinese character prediction in old documents.
To adapt character detection to multi-scale locations precisely, minimizing pre-processing steps.

Main Methods:

Utilized a neural network with multi-scale feature maps to adapt to character locations.
Employed pre-selected bounding boxes of varying scales and aspect ratios across feature maps.
Applied non-maximum suppression to prune bounding boxes for final results.

Main Results:

Achieved enhanced detection performance with a detection rate (DT) of 98.84%, false positive per character (FPPC) of 0.71, and F-score of 97.64% on Caoshu, Character, and Src-images datasets.
Demonstrated superior performance compared to the Single-Shot Detector (SSD) model.

Conclusions:

The proposed method significantly enhances handwritten Chinese character detection in historical documents.
The optimized neural network architecture and loss function contribute to improved detection accuracy and efficiency.