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Recognition of Arabic Handwritten Words using Gabor-based Bag-of-Features Framework
2018
International Journal of Computing and Digital Systems
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In this work we present a system for the automatic recognition of Arabic handwritten words based on statistical features extracted by Bag-of-Features framework that exploits the discriminative power of ...
The effective parameters of the two layouts as well as the Bag-of-Features framework are experimentally evaluated and the optimal values are used in reporting the final recognition accuracies. ...
We would also acknowledge the support provided by King Fahd University of Petroleum & Minerals (KFUPM) through project number RG 1313-1/2. ...
doi:10.12785/ijcds/070104
fatcat:y7kahbbzzjb6lbfd6m5ze5t34u
Comparison of convolutional neural network and bag of features for multi-font digit recognition
2019
Indonesian Journal of Electrical Engineering and Computer Science
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<span>This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. ...
Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text. ...
ACKNOWLEDGEMENTS The authors would like to thank the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, for sponsoring this research. ...
doi:10.11591/ijeecs.v15.i3.pp1322-1328
fatcat:7qzlpqbgozhlzgt73djubg2kbm
A Novel Technique for Handwritten Digit Recognition Using Deep Learning
2023
Journal of Sensors
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Handwritten digit recognition (HDR) shows a significant application in the area of information processing. ...
In the proposed method, we have tried to overcome the aforementioned limitations by introducing a deep learning- (DL-) based technique, namely, EfficientDet-D4, for numeral categorization. ...
Acknowledgments The authors are thankful for the support from The FAMLIR Group, the University of Lahore, Lahore, Pakistan, and the University Institute of Information Technology, Pir Mehr Ali Shah Arid ...
doi:10.1155/2023/2753941
fatcat:jlriyf3ovbds5lgzn2gcnsnjqe
Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit
2023
Artificial Intelligence and Applications
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We get more precise results in digit recognition, thanks to deep learning convolutional neural networks (CNNs), which function similarly to the human brain. ...
Devanagari character and digit recognition are a difficult undertaking because writing style depends on a person's traits and differs from person to person. ...
Deep learning approach has been used by many researchers for recognition of handwritten character or digits (Pandey, 2021) . ...
doi:10.47852/bonviewaia3202441
fatcat:qygze3lyefcinmzcsj5rltyphi
Learning to count with deep object features
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from ...
We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. * S. Seguí, O. Pujol and J Vitrià are with the ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Tesla K40 GPU used for this research. ...
doi:10.1109/cvprw.2015.7301276
dblp:conf/cvpr/SeguiPV15
fatcat:ackgxxegjvfubcxfy4mzwxt5fa
A Survey on Arabic Handwritten Script Recognition Systems
2021
International Journal of Artificial Intelligence and Machine Learning
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We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. ...
In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. ...
Use of deep Learning: Despite recent remarkable accuracy, relatively very less frameworks have been developed using deep learning for Arabic handwritten character recognition, this learning architecture ...
doi:10.4018/ijaiml.20210701.oa9
fatcat:eyf77ynim5cpdi6fkgwhmm656m
Real time handwriting recognition system using CNN algorithms
2023
Wasit Journal of Computer and Mathematics Science
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Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. ...
Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. ...
The experimental setup includes recognition of handwritten documents using deep learning models, with the focus on the Bangla language, which lacks handwritten word datasets. ...
doi:10.31185/wjcms.157
fatcat:aljvshjflbbyjcme3rb2wm6pt4
Learning to count with deep object features
[article]
2015
arXiv
pre-print
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In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from ...
To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Tesla K40 GPU used for this research. ...
arXiv:1505.08082v1
fatcat:fmjjbmwbjzezno5ukhc5ynsi7i
Deep Learning for Classification and as Tapped-Feature Generator in Medieval Word-Image Recognition
2018
2018 13th IAPR International Workshop on Document Analysis Systems (DAS)
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This paper aims to investigate the problem of recognizing words from historical document images using a deep-learning based framework for feature extraction and classification while countering the problem ...
Such digital archives can be more useful if automatic indexing and retrieval of document images can be provided to the end users of a digital library. ...
jointly funded by the Dutch, French, Spanish research authorities in the framework of the "Joint Programming Initiative on Cultural Heritage and Global Change". ...
doi:10.1109/das.2018.82
dblp:conf/das/ChandaOHSS18
fatcat:mf4dtlpnyfexdflbcm6zfelkei
An efficient CNN model with squirrel optimizer for handwritten digit recognition
2021
International Journal of Advanced Technology and Engineering Exploration
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Conflicts of interest The authors have no conflicts of interest to declare. ...
But, the existence of huge variations encountered in handwritten digit prediction problems uplifts the need and adoption of the deep learning-based classification model. ...
The recent advancement in the technological field motivates to perform the handwritten digit prediction process using Deep Learning (DL) as it gives higher prediction accuracy than existing Machine Learning ...
doi:10.19101/ijatee.2021.874073
fatcat:aqlqzlvftzf5tmrnmclswaug3i
End-to-End Optical Character Recognition for Bengali Handwritten Words
[article]
2021
arXiv
pre-print
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Optical character recognition (OCR) is a process of converting analogue documents into digital using document images. ...
The proposed architecture implements an end to end strategy that recognises handwritten Bengali words from handwritten word images. ...
They use only 210 samples of handwritten Bengali words to run the experiment, and they claim 95.32% average accuracy. Pramanik R., Bag S. ...
arXiv:2105.04020v1
fatcat:h5dzzxbqn5bb5dmgf7a7rgfkuu
Deep sparse auto-encoder features learning for Arabic text recognition
2021
IEEE Access
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We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition ...
), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST). ...
Our analysis of the OCR literature review for Arabic script led us to select the deep SAE-based BoF as the core framework used for feature extraction and HMMs for recognition. ...
doi:10.1109/access.2021.3053618
fatcat:p7jhbokjsjbunceuq4lu7xnmci
Handwriting Recognition in Low-resource Scripts using Adversarial Learning
[article]
2019
arXiv
pre-print
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Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes. ...
We test our meta-framework, which is built on top of popular word-spotting and word-recognition frameworks and enhanced by the AFDM, not only on extensive Latin word datasets but also sparser Indic scripts ...
Instead, we propose an adversarial-learning based framework for handwritten word retrieval tasks for low resource scripts in order to train deep networks from a limited number of samples. ...
arXiv:1811.01396v5
fatcat:xp3emb4whrh7jasluh3wv3ffce
Towards the effectiveness of Deep Convolutional Neural Network based Fast Random Forest Classifier
[article]
2016
arXiv
pre-print
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Several publicly available Bioinformatics dataset, Handwritten digits recognition and Image segmentation dataset are considered for evaluation of the proposed approach. ...
The excellent performance obtained by the proposed DCNN based feature selection with FRF classifier on high dimensional datasets makes it a fast and accurate classifier in comparison the state-of-the-art ...
Zeng et al. (2015) [16] Proposed to use Convolutional neural network on ISH images with the application of invariant feature extractors and bag-of-words method for obtaining better gene expression ...
arXiv:1609.08864v1
fatcat:vbmybz65kzeuzou6stpiqqyugy
Subword Recognition in Historical Arabic Documents using C-GRUs
2021
TEM Journal
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This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents. ...
More specifically, we introduce a hybrid CNN-GRU model where the shallow convolutional network learns robust feature representations while the GRU layers carry out the sequence modelling and generate the ...
The statements made herein are solely the responsibility of the authors. ...
doi:10.18421/tem104-19
fatcat:v4z4eyksevb4pedqqiozfyatky
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