Application and optimization of machine learning algorithms for optical character recognition in complex scenarios

In the era of artificial intelligence, the technology of optical character recognition under complex backgrounds has become particularly important. This article investigated how machine learning algorithms can improve the accuracy of text recognition in complex scenarios. By analyzing algorithms suc...

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Veröffentlicht in:Journal of intelligent systems Jg. 34; H. 1; S. 161 - 84
Hauptverfasser: Liu, Liming, Yang, Dexin, Chen, Juntao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin De Gruyter 01.01.2025
Walter de Gruyter GmbH
Schlagworte:
Accuracy
Algorithms
Artificial intelligence
automated machine learning algorithms
Automation
Big Data
Cluster analysis
Clustering
Data processing
Deep learning
Efficiency
Machine learning
machine learning algorithms
Neural networks
Optical character recognition
Software
Support vector machines
system implementation
User satisfaction
Vector quantization
ISSN:2191-026X, 0334-1860, 2191-026X
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Zusammenfassung:In the era of artificial intelligence, the technology of optical character recognition under complex backgrounds has become particularly important. This article investigated how machine learning algorithms can improve the accuracy of text recognition in complex scenarios. By analyzing algorithms such as scale-invariant feature transform, -means clustering, and support vector machine, a system was constructed to address the challenges of text recognition under complex backgrounds. Experimental results show that the proposed algorithm achieves 7.66% higher accuracy than traditional algorithms, and the built system is fast, powerful, and highly satisfactory to users, with a 13.6% difference in results between the two groups using different methods. This indicates that the method proposed in this study can effectively meet the needs of complex text recognition, significantly improving recognition efficiency and user satisfaction.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2191-026X
0334-1860
2191-026X
DOI:10.1515/jisys-2023-0307