Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Latif, Ghazanfar; Alghazo, Jaafar; Alzubaidi, Loay; Naseer, Muzammal; Alghazo, Yazan

doi:10.1109/asar.2018.8480289

Cited by 34 publications

(13 citation statements)

References 17 publications

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“…Although in our previous work [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] we were able to achieve high recognition ratios, our mission in this research is to extend our findings and to build upon our previous results. Specifically, the objective of this work is to build a universal numeral recognizer; one that can recognize both printed and handwritten multilingual numerals.…”

Section: Background and State-of-the-art Researchmentioning

confidence: 56%

“…They utilized it for deep features extraction. On the other hand, in [49] the authors used Deep Recurrent Convolutional Neural Network (DRCNN) [50] as a predictor model for bankruptcy, while in [51] the authors built their own CNN structure model based on Efficient Net architecture with nine levels (nine layers) to classify X-ray images into three classes (Normal, Pneumonia, and COVID- 19). Table 34 shows the differences and the similarities between the proposed structure and the existing structures in literature.…”

Section: Discussionmentioning

confidence: 99%

“…The authors of [19] employed deep learning techniques for the recognition of numerals from multiple languages including Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic, where they were able to achieve high recognition rates. The author of [20] adopted the restricted Boltzmann machine (RBM) in his work as the representative deep learning methodology for the recognition of English and Devanagari numerals with an SVM-based classifier.…”

Section: Background and State-of-the-art Researchmentioning

confidence: 99%

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Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

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Section: Background and State-of-the-art Researchmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Background and State-of-the-art Researchmentioning

confidence: 99%

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Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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“…MADBase dataset was used for testing and results showed significant improvement over different classification algorithms. In [15], the authors developed a CNN for the recognition of mutli-language numerals in the following languages (English, Arabic, Persian, Urdu, and Devanagari). The overall accuracy of the combined dataset was 99.26% with a precision of 99.29%.…”

Section: Enhanced Numeral Recognition For Handwritten Multi-language mentioning

confidence: 99%

Enhanced Numeral Recognition for Handwritten Multi-language Numerals Using Fuzzy Set-Based Decision Mechanism

Al-Hmouz¹,

Latif²,

Alghazo³

et al. 2020

IJMLC

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Handwritten character and numeral recognition have gained interest in the research community as part of the big picture of Machine Learning. Writer independent recognition systems are still in the working and the research is geared towards an optimized technique that can achieve this. In this paper, we propose a numeral recognition system that forms fuzzy sets of the features extracted using modified structural features for English, Arabic, Persian, and Devanagari Numerals. The structural features extract the geometrical primitives that distinguish each image. After the feature extraction phase, the results are input into a classifier, we test two different classifiers namely Neural Network and Naïve Base. To further enhance the recognition process with low overhead the erroneously recognized numerals (confusion matrix) are processed through the fuzzy set-based decision mechanism to enhance the numeral recognition process. Results indicate that recognition is enhanced by applying the fuzzy set-based decision mechanism for both classifer.

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“…In particular, deep learning and reinforcement learning have wide application in several fields, including robotics. Ap plication such as facial recognition [7], detection of energy streams [8], modelling of surface roughness [9], and Brain MR Image Classification [10] uses deep neural network that learn a model to classify fu ture data.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning of a CableBased Parallel Robot using Reinforcement Learning and Soft Actor-Critic

Al-smadi

2020

Wseas Transactions on Applied and Theoretical Mechanics

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Industry 4.0 introduces the use of modular stations and better communication between agents to improve manufacturing efficiency and to lower the downtime between the customer and its final product. Among novel mechanisms that have a high potential in this new industrial paradigm are cablesuspended parallel robot (CSPR): their payloadtomass ratio is high compared to their serial robot counterpart and their setup is quick compared to other types of parallel robots such as Gantry system, popular in the automotive industry but difficult to set up and to adapt while the production line changes. A CSPR can cover the workspace of a manufacturing hall and providing assistance to operators before they arrive at their workstation. One challenge is to generate the desired trajectories, so that the CSPR could move to the desired area. Reinforcement Learning (RL) is a branch of Artificial Intelligence where the agent interacts with an environment to maximize a reward function. This paper proposes the use of a RL algorithm called Soft ActorCritic (SAC) to train a two degreesoffreedom (DOFs) CSPR to perform pickandplace trajectory. Even though the pickandplace trajectory based on artificial intelligence has been an active research with serial robots, this technique has yet to be applied to parallel robots.

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Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Cited by 34 publications

References 17 publications

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Enhanced Numeral Recognition for Handwritten Multi-language Numerals Using Fuzzy Set-Based Decision Mechanism

Trajectory Planning of a CableBased Parallel Robot using Reinforcement Learning and Soft Actor-Critic

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