Arabic literal amount sub-word recognition using multiple features and classifiers
by Irfan Ahmad; Sameh Awaida; Sabri A. Mahmoud
International Journal of Applied Pattern Recognition (IJAPR), Vol. 6, No. 2, 2020

Abstract: Bank check processing is an important application of document analysis and recognition. Recognising the literal amounts from the check images is challenging and an open research problem. In this paper, we present our work on Arabic bank check literal amounts' sub-word recognition using four sets of features and three classifiers namely: support vector machine (SVM), neural network (NN), and decision tree forest (DTF) classifiers. In addition, we investigated two different approaches for classifier fusion. We tested our system on the CENPARMI database of Arabic bank check images. Our recognition results outperform previous published results on the same database.

Online publication date: Mon, 30-Nov-2020

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