OCR based image text to speech conversion using FKM and comparing with FCM clustering algorithm.
In: AIP Conference Proceedings, Jg. 2853 (2024-04-25), Heft 1, S. 1-7
Konferenz
Zugriff:
The proposed project is a comparative study of the accuracy of OCR-based image-to-speech conversion using novel FKM and FCM algorithms from low-resolution images that have high accuracy. Method and material: Two classes are contrasted, novel Fuzzy k-means clustering (FKM) (N=10) and Fuzzy c-means clustering (FCM) (N=10) with the G Power software, the total volume is determined with alpha equal to 0.05, the enrollment ratio is equal to 0.1, the confidence interval is 95%, and the power is 80%. SPSS' software was used to conduct an independent sample t-test to assess the accuracy rate. Conclusion: The results of the MATLAB simulation are 90 % accurate and have a precision of 84 % in the conversion of text to speech. In SPSS' statistical analysis, a significant degree of success (0.001) was achieved. Conclusion: A novel FKM classifier is demonstrated to be superior to the FCM Classifier for OCR-based images to speech. [ABSTRACT FROM AUTHOR]
| Titel: |
OCR based image text to speech conversion using FKM and comparing with FCM clustering algorithm.
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| Autor/in / Beteiligte Person: | Babu, M. Pandu ; Malathi, K. ; Nalini, N. |
| Zeitschrift: | AIP Conference Proceedings, Jg. 2853 (2024-04-25), Heft 1, S. 1-7 |
| Quelle: | 2024, Vol. 2853 Issue 1, p1-7. 7p.; Jg. 2853 (2024-04-25) 1, S. 1-7 |
| Veröffentlichung: | 2024 |
| Medientyp: | Konferenz |
| ISSN: | 0094-243X (print) |
| DOI: | 10.1063/5.0198466 |
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