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Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods.

Sana Shokat1, Rabia Riaz1, Sanam Shahla Rizvi2

  • 1Department of Computer Science and IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning system for English Braille recognition using a touchscreen dataset. Reconstruction Independent Component Analysis (RICA) significantly improved accuracy in identifying Braille patterns.

Keywords:
Braille patternsDecision TreeKNNPCA featuresRICA featuresSVMmachine learningtext conversionvisually impaired

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Braille is a crucial communication method for the visually impaired.
  • Technological advancements are enhancing Braille reading and writing tools.
  • Existing systems may benefit from improved machine learning approaches for pattern recognition.

Purpose of the Study:

  • To develop and evaluate a machine learning system for English Braille pattern identification.
  • To compare the effectiveness of different feature extraction methods for Braille recognition.
  • To utilize a robust dataset collected from visually impaired students.

Main Methods:

  • Collected an English Braille Grade-1 dataset from visually impaired students using a touchscreen.
  • Employed machine learning algorithms: Support Vector Machine (SVM), Decision Trees (DT), and K-Nearest Neighbor (KNN).
  • Utilized Reconstruction Independent Component Analysis (RICA) and Principal Component Analysis (PCA) for feature extraction, comparing their performance.

Main Results:

  • The Reconstruction Independent Component Analysis (RICA) based feature extraction method outperformed PCA, Random Forest, and Sequential methods.
  • The system achieved high accuracy in recognizing English Braille patterns.
  • Statistical tests confirmed the significance of the obtained results.

Conclusions:

  • The developed machine learning system, particularly with RICA, demonstrates a promising approach for accurate English Braille recognition.
  • This technology has the potential to enhance assistive tools for the visually impaired.
  • Further research can explore advanced algorithms and larger datasets for improved performance.