Classification of Test Documents Based on Handwritten Student ID's Characteristics (CROSBI ID 624473)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Čelar, Stipe ; Stojkić, Željko ; Šeremet, Željko ; Marušić, Željko ; Zelenika, Danijel
engleski
Classification of Test Documents Based on Handwritten Student ID's Characteristics
The bag of words (BoW) model is an efficient image representation technique for image categorization and annotation tasks. Building good feature vocabularies from automatically extracted image feature vectors produces discriminative feature words, which can improve the accuracy of image categorization tasks. In this paper we use feature vocabularies based biometric characteristic for identification on student ID and classification of students' papers and various exam documents used at the University of Mostar. We demonstrated an experiment in which we used OpenCV as an image processing tool and tool for feature extraction. As regards to classification method, we used Neural Network for Recognition of Handwritten Digits (student ID). We tested out proposed method on MNIST test database and achieved recognition rate of 94, 76% accuracy. The model is tested on digits which are extracted from the handwritten student exams and the accuracy of 82% is achieved (92% correctly classified digits).
Bag of Words ; Image categorization ; Neural Network ; Recognition of Handwritten Digits ; Visual vocabularie
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Podaci o prilogu
782-790.
2015.
objavljeno
Podaci o matičnoj publikaciji
Procedia Engineering, Volume 100-2015
Katalinic, B.
Beč: Elsevier
1876-7058
Podaci o skupu
Nepoznat skup
predavanje
29.02.1904-29.02.2096
Povezanost rada
Računarstvo