Jove
Visualize
Contact Us

Related Experiment Videos

Hidden Markov models for character recognition.

J A Vlontzos1, S Y Kung

  • 1Siemens Corp. Res., Cholargos.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization.

IEEE transactions on neural networks·2008
Same author

Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification.

IEEE transactions on neural networks·2008
Same author

Multi-metric and multi-substructure biclustering analysis for gene expression data.

Proceedings. IEEE Computational Systems Bioinformatics Conference·2006
Same author

Accurate detection of aneuploidies in array CGH and gene expression microarray data.

Bioinformatics (Oxford, England)·2004
Same author

A comparative study on kernel-based probabilistic neural networks for speaker verification.

International journal of neural systems·2002
Same author

Analytical model of a three-dimensional integral image recording system that uses circular- and hexagonal-based spherical surface microlenses.

Journal of the Optical Society of America. A, Optics, image science, and vision·2001
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

This study introduces a hierarchical system for character recognition using hidden Markov models. The novel approach achieves 97-99% accuracy for real-time multifont and handwriting recognition.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Character recognition systems often struggle with context sensitivity and character variations.
  • Existing methods may not efficiently handle multifont, multisize, or handwritten characters in real-time.

Purpose of the Study:

  • To present a novel hierarchical system for character recognition.
  • To address challenges in context sensitivity and character instantiation within recognition systems.
  • To enable high-accuracy, real-time recognition of diverse character types.

Main Methods:

  • Development of a hierarchical system incorporating hidden Markov model (HMM) knowledge sources.
  • Implementation of a two-level architecture for enhanced processing.

Related Experiment Videos

  • Utilizing a systolic array for parallel processing and real-time performance.

    • Main Results:

    • Achieved high accuracy rates of 97-99% for character recognition.
    • Demonstrated real-time processing capability at 1 millisecond per character.
    • Successfully implemented multifont, multisize printed character recognition.
    • Enabled effective handwriting recognition.

    Conclusions:

    • The proposed hierarchical system effectively solves context sensitivity and character instantiation problems.
    • The system's architecture and systolic array implementation facilitate efficient, high-accuracy, real-time character recognition.
    • This approach shows significant potential for applications in document processing and human-computer interaction.