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Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
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Related Experiment Video

Updated: Apr 18, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Historical Manuscripts Analysis: A Deep Learning System for Writer Identification Using Intelligent Feature Selection

Merouane Boudraa¹, Akram Bennour¹, Mouaaz Nahas²

¹Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa 12000, Algeria.

Journal of Imaging

|June 25, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning system using vision transformers to identify historical manuscript writers. The method enhances historical document analysis and writer identification accuracy.

Keywords:

deep learning historical manuscripts intelligent features transfer learning vision transformers writer identification

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Area of Science:

Computer Science
Digital Humanities
Historical Document Analysis

Background:

Identifying historical manuscript writers is vital for historical research and solving mysteries.
Existing methods may lack the precision needed for complex historical document analysis.

Purpose of the Study:

To develop and evaluate a deep learning system for historical manuscript writer identification.
To assess the effectiveness of vision transformers and feature selection techniques in this domain.

Main Methods:

Document preprocessing included bilateral filtering and Otsu thresholding.
Feature extraction used the FAST detector and k-means clustering for uniform patches.
Vision transformer models were employed for classification of handwriting patterns.

Main Results:

The system demonstrated robust performance in classifying historical manuscripts by writer.
Vision transformers showed superior capability in learning complex patterns from manuscript data.
The approach outperformed state-of-the-art methods on the ICDAR 2017 dataset.

Conclusions:

The developed deep learning system is a powerful tool for historical manuscript analysis.
Vision transformers represent a significant advancement in automated historical document analysis.
This research offers a novel solution for historical writer identification challenges.