BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
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TLDR
Sufian et al. as mentioned in this paper proposed a task-oriented model called Bengali handwritten numeral digit recognition based on densely connected convolutional neural networks (BDNet), which is used to classify (recognize) Bengali numeric digits.About:
This article is published in Journal of King Saud University - Computer and Information Sciences.The article was published on 2022-06-01 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Bengali & Numeral system.read more
Citations
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Journal ArticleDOI
Two Decades of Bengali Handwritten Digit Recognition: A Survey
TL;DR: In this article , the characteristics and inherent ambiguities of Bengali handwritten digits along with a comprehensive insight of two decades of the state-of-the-art datasets and approaches towards offline BHDR have been analyzed.
Proceedings ArticleDOI
Manual and Automatic Feature Engineering in Digital Image Forgery Detection Algorithms: Survey
TL;DR: In this article , a comparative analysis of image splicing by gathering several techniques and classifying them according to the features they employed, based on manual engineering features and automatic engineering features.
Proceedings ArticleDOI
Recognition of Bengali Handwritten Digits Using Spiking Neural Network Architecture
Book ChapterDOI
Discrete Wavelet-Based Multi-Classifier Approach for Recognition of Offline Handwritten Hindi Numerals
Danveer Rajpal,Akhil Ranjan Garg +1 more
TL;DR: In this article , a bi-orthogonal Discrete wavelet transform (DWT) was used for important feature extraction and multiple classifiers, namely, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the classification task.
Journal ArticleDOI
A time efficient offline handwritten character recognition using convolutional extreme learning machine
TL;DR: In this article , a convolutional layer-based Extreme Learning Machine (CELM) architecture has been designed and implemented to recognize handwritten characters and reduce execution time, which achieved an accuracy of 91.76, 94.12, and 91.43%.
References
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TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
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