ahcd proposed dataset Search Results


ahcd proposed dataset  (SoftMax Inc)
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SoftMax Inc ahcd proposed dataset
A summary of related work on handwritten Arabic character recognition for adult writers.
Ahcd Proposed Dataset, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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ahcd proposed dataset - by Bioz Stars, 2026-04
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A summary of related work on handwritten Arabic character recognition for adult writers.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: A summary of related work on handwritten Arabic character recognition for adult writers.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

A summary of related work on handwritten Arabic character recognition for child writers.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: A summary of related work on handwritten Arabic character recognition for child writers.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Description of the used datasets.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Description of the used datasets.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques: Isolation

Some preprocessed Hijja and AHCD character data samples: ( a ) Child writers’ samples; ( b ) Adult writers’ samples.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Some preprocessed Hijja and AHCD character data samples: ( a ) Child writers’ samples; ( b ) Adult writers’ samples.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

An overview of conducted experimental work.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: An overview of conducted experimental work.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Statistics of the used datasets.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Statistics of the used datasets.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques: Biomarker Discovery

Child character recognition results of Experiment 1, using Hijja for training and testing.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Child character recognition results of Experiment 1, using Hijja for training and testing.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Child character recognition results of Experiment 2, using AHCD for training and Hijja for testing.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Child character recognition results of Experiment 2, using AHCD for training and Hijja for testing.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Child character recognition results of Experiment 3, using combined Hijja and AHCD for training and Hijja for testing.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Child character recognition results of Experiment 3, using combined Hijja and AHCD for training and Hijja for testing.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Writer-group classification performance of Experiment 4, without supplementary features using combined Hijja and AHCD for training and testing.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Writer-group classification performance of Experiment 4, without supplementary features using combined Hijja and AHCD for training and testing.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Writer-group classification performance of Experiment 5, with supplementary features using combined Hijja and AHCD for training and testing.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Writer-group classification performance of Experiment 5, with supplementary features using combined Hijja and AHCD for training and testing.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques:

Comparison between our proposed methodology and current approaches in the literature.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

doi: 10.3390/s23156774

Figure Lengend Snippet: Comparison between our proposed methodology and current approaches in the literature.

Article Snippet: [ ] , 2021 , CNN , Softmax , AHCD Proposed dataset Hijja MNIST , Characters Characters Characters Digits , 16,800 38,100 47,434 70,000 , 99% 95.4% 90% 99%.

Techniques: Comparison, Extraction