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

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.6K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.6K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Proofreading01:31

Proofreading

6.3K
Synthesis of new DNA molecules is carried out by the enzyme DNA polymerase, which adds nucleotides on the daughter strand complementary to the template DNA strand. DNA polymerase has a higher affinity to add the correct base and ensures fidelity during DNA replication. Furthermore,  it exhibits proofreading activity during replication, using an exonuclease domain that cuts off incorrect nucleotides from the nascent DNA strand.
Errors During Replication are Corrected by the DNA Polymerase...
6.3K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.1K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.4K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.4K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Unsteady CFD simulation of a rotor blade under various wind conditions.

Scientific reportsยท2024
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

792

Correcting spelling mistakes in Persian texts with rules and deep learning methods.

Sa Kasmaiee1, Si Kasmaiee2, M Homayounpour2

  • 1Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran. saman.kasmaiee@aut.ac.ir.

Scientific Reports
|November 15, 2023
PubMed
Summary

This study developed two systems for Persian spell checking: a rule-based system and a deep learning model. The deep learning approach achieved high accuracy, demonstrating effective automatic spelling correction for Persian text.

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

603
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Related Experiment Videos

Last Updated: Jul 11, 2025

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

792
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

603
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Automatic spelling correction is crucial for improving the quality of digital text.
  • Persian language processing presents unique challenges due to its script and morphology.
  • Existing methods for Persian spell checking require enhancement for better accuracy.

Purpose of the Study:

  • To develop and evaluate two distinct systems for automatic Persian spelling error correction.
  • To compare the effectiveness of a rule-based approach against a deep learning model.
  • To enhance a deep neural network with advanced layers for improved performance.

Main Methods:

  • A rule-based system utilizing a common misspelling list (700 entries) and a Persian word database (55,000 words) with 112 correction rules.
  • A deep learning approach employing an encoder-decoder network with Long Short-Term Memory (LSTM) and FastText word embeddings.
  • Enhancement of the deep learning model by incorporating convolutional and capsule layers, trained on 800,000 sentences.

Main Results:

  • The rule-based system used 112 rules and evaluated on 2500 sentences.
  • The deep learning model, including enhanced versions with capsule and convolutional layers, showed comparable performance to the base network.
  • The deep learning model achieved notable evaluation scores: 87% accuracy, 70% precision, 89% recall, 78% F-measure, and 84% Bilingual Evaluation Understudy (BLEU).

Conclusions:

  • Both rule-based and deep learning methods can be applied to Persian spell correction.
  • The deep learning approach, particularly with enhancements, demonstrates strong potential for accurate automatic spelling correction.
  • The study provides valuable insights into advanced techniques for natural language processing in the Persian language.