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Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

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Related Experiment Video

Updated: May 8, 2026

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
05:58

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Published on: March 11, 2021

Handwritten Chinese/Japanese text recognition using semi-Markov conditional random fields.

Xiang-Dong Zhou1, Da-Han Wang, Feng Tian

  • 1Beijing Key Lab of Human-Computer Interaction,Institute of Software, Chinese Academy of Sciences, Beijing, P.R.China. xiangdong@iscas.ac.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-Markov conditional random field (semi-CRF) method for recognizing handwritten Chinese and Japanese text. The approach achieves high accuracy, outperforming existing systems in competitions.

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Related Experiment Videos

Last Updated: May 8, 2026

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
05:58

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Published on: March 11, 2021

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

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Published on: August 9, 2024

Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Machine Learning

Background:

  • Handwritten text recognition (HTR) is challenging due to variations in writing styles.
  • Existing HTR methods often struggle to effectively integrate character recognition with contextual information.

Purpose of the Study:

  • To develop an advanced HTR method for Chinese and Japanese handwritten text.
  • To improve recognition accuracy by fusing character-level scores with contextual compatibilities.

Main Methods:

  • Proposes a high-order semi-Markov conditional random field (semi-CRF) model.
  • Defines the model on a lattice of segmentation-recognition hypotheses.
  • Optimizes fusion parameters using negative log-likelihood loss with a margin term.
  • Employs forward-backward lattice pruning and beam search for computational efficiency.

Main Results:

  • Achieved 95.20% character-level correct rate on CASIA-OLHWDB (Chinese).
  • Achieved 95.44% character-level correct rate on TUAT Kondate (Japanese).
  • Outperformed the best system in the ICDAR 2011 Chinese handwriting recognition competition.

Conclusions:

  • The proposed semi-CRF method effectively integrates character recognition and contextual information for HTR.
  • Demonstrates superior performance on unconstrained online handwritten text compared to state-of-the-art systems.