Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis

Omid Bonakdar Sakhi

Open source

DOI: 10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8
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Publisher: Agence Bibliographique de l'Enseignement Supérieur
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Cite this work

BibTeX

@article{allodium:10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8,
  title = {Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis},
  author = {Omid Bonakdar Sakhi},
  doi = {10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8},
  url = {https://doi.org/10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8}
}

RIS

TY  - JOUR
TI  - Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis
AU  - Omid Bonakdar Sakhi
DO  - 10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8
UR  - https://doi.org/10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8
ER  -

APA

Sakhi, O. B. (n.d.). Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis. https://doi.org/10.70675/4d7a3a50z55ffz479dz9f12zfcbb3d0b9aa8

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crossref · retrieved 2026-11-10T17:59:26.755Z