Jove
Visualize
Contact Us
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

Related Concept Videos

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

95.4K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
95.4K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.4K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.4K
Stereotype Content Model02:16

Stereotype Content Model

15.5K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.5K
DC Generator01:19

DC Generator

2.1K
An alternator converts mechanical energy into electrical energy that varies sinusoidally, resulting in AC current. Meanwhile, a DC generator converts mechanical energy into electrical energy, which are DC pulses with the same polarity. The construction of a DC generator is similar to that of an alternator, except that the pair of slip rings is replaced by a single split ring, also called a commutator. The commutator functions like a periodic rotary switch; it changes the contacts with the...
2.1K
Next-generation Sequencing03:00

Next-generation Sequencing

98.6K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
98.6K
Generation Time01:22

Generation Time

1.5K
Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
1.5K

You might also read

Related Articles

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

Sort by
Same author

AI-based bi-modal fusion system for automated clinical pain monitoring.

Computers in biology and medicine·2025
Same author

Robot System Assistant (RoSA): evaluation of touch and speech input modalities for on-site HRI and telerobotics.

Frontiers in robotics and AI·2025
Same author

Multi-Head Attention-Based Framework with Residual Network for Human Action Recognition.

Sensors (Basel, Switzerland)·2025
Same author

Mobile Robot Navigation with Enhanced 2D Mapping and Multi-Sensor Fusion.

Sensors (Basel, Switzerland)·2025
Same author

Experimental economics for machine learning-a methodological contribution on lie detection.

PloS one·2024
Same author

A review of visual SLAM for robotics: evolution, properties, and future applications.

Frontiers in robotics and AI·2024

Related Experiment Video

Updated: Feb 6, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.6K

Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting.

Moftah Elzobi1, Ayoub Al-Hamadi2

  • 1Institute for Information Technology and Communications (IIKT), Otto von Guericke University, 39106 Magdeburg, Germany. moftah.elzobi@ovgu.de.

Sensors (Basel, Switzerland)
|August 29, 2018
PubMed
Summary

This study compares generative and discriminative handwritten word recognition models. Discriminative models like Conditional Random Fields (CRF) and Hidden-State CRF (HCRF) show promise for improved Arabic handwriting recognition.

Keywords:
Arabic OCRCRFHCRFHMMoffline handwriting recognition

More Related Videos

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

8.2K
Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
05:52

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

15.5K

Related Experiment Videos

Last Updated: Feb 6, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.6K
Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

8.2K
Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
05:52

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

15.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Handwritten word recognition often uses generative models.
  • Discriminative learning offers a theoretically more effective approach.
  • Existing methods lack robust rejection of incorrect segmentations.

Purpose of the Study:

  • Compare generative Hidden Markov Models (HMM) with discriminative Conditional Random Fields (CRF) and Hidden-State CRF (HCRF) for offline Arabic handwritten word recognition.
  • Evaluate the performance of these models on diverse datasets.
  • Introduce adaptive threshold schemes to improve HMM rejection capabilities.

Main Methods:

  • Trained and applied HMM classifiers with adaptive threshold schemes.
  • Introduced and assessed CRF and HCRF classifiers for Arabic handwriting recognition.
  • Evaluated all three strategies using two distinct databases, analyzing both word and letter recognition.

Main Results:

  • Discriminative models (CRF, HCRF) were assessed against generative HMM.
  • Adaptive threshold schemes were proposed to enhance HMM performance.
  • Performance comparison highlighted the strengths and weaknesses of each recognition strategy.

Conclusions:

  • Discriminative approaches offer a potentially superior alternative to generative models for handwritten word recognition.
  • CRF and HCRF present viable options for Arabic handwriting recognition tasks.
  • Further research can build upon these findings to optimize recognition systems.