Open Access Open Access  Restricted Access Subscription or Fee Access

The Relevant CNNs Features Based HMM for Arabic Handwriting Recognition

M. Amrouch, M. Rabi

Abstract



This paper presents a work that aims to compare the learning features with Convolutional Neural Networks (CNN) and the handcrafted features. In order to determine the relevant features among of those kinds. We consider our previous baseline HMM system Rabi et al. 2016, for Arabic handwriting recognition. Experiments have been performed on the benchmark IFN/ENIT database. Obtained results with CNN features surpass those achieved using the hand-crafted features. This demonstrates the high efficiency of CNN results from the strong capability for hierarchical feature learning given a large amount of data. However, Hand-crafted features are not extracted from an optimization process to be adequate with the specific problem, and insufficient to be encoded with supervision.

Keywords


Handwriting Recognition, Extraction features, Convolutional Neural Networks, Hidden Markov Models.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.