Summary of Arabic Handwritten Text For Person Biometric Identification: a Deep Learning Approach, by Mazen Balat et al.
Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach
by Mazen Balat, Youssef Mohamed, Ahmed Heakl, Ahmed Zaky
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper compares three advanced deep learning models (ResNet50, MobileNetV2, and EfficientNetB7) on recognizing Arabic handwritten text for person biometric identification. The models are tested on three datasets: AHAWP, Khatt, and LAMIS-MSHD. Results show that EfficientNetB7 outperforms the others, achieving high test accuracies of 98.57%, 99.15%, and 99.79% respectively. EfficientNetB7’s exceptional performance is attributed to its innovative techniques such as compound scaling, depth-wise separable convolutions, and squeeze-and-excitation blocks. These features enable the model to extract more abstract and distinctive features from handwritten text images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well deep learning models can recognize Arabic handwriting for person identification. It compares three different models on three datasets. The best model is EfficientNetB7, which gets really good results. This is because it uses special techniques that help it understand the handwriting better. These techniques are important because they make it easier to tell people apart. |
Keywords
» Artificial intelligence » Deep learning
