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A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition. (arXiv:2201.10102v1 [cs.CV])
Web: http://arxiv.org/abs/2201.10102
Jan. 26, 2022, 2:11 a.m. | Md. Ferdous Wahid, Md. Fahim Shahriar, Md. Shohanur Islam Sobuj
cs.LG updates on arXiv.org arxiv.org
Bangla Handwritten Digit recognition is a significant step forward in the
development of Bangla OCR. However, intricate shape, structural likeness and
distinctive composition style of Bangla digits makes it relatively challenging
to distinguish. Thus, in this paper, we benchmarked four rigorous classifiers
to recognize Bangla Handwritten Digit: K-Nearest Neighbor (KNN), Support Vector
Machine (SVM), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT)
based on three handcrafted feature extraction techniques: Histogram of Oriented
Gradients (HOG), Local Binary Pattern (LBP), and Gabor …
More from arxiv.org / cs.LG updates on arXiv.org
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