This study thoroughly investigates how well deep learning models can
recognize Arabic handwritten text for person biometric identification. It
compares three advanced architectures -- ResNet50, MobileNetV2, and
EfficientNetB7 -- using three widely recognized datasets: AHAWP, Khatt, and
LAMIS-MSHD. Results show that EfficientNetB7 outperforms the others, achieving
test accuracies of 98.57\%, 99.15\%, and 99.79\% on AHAWP, Khatt, and
LAMIS-MSHD datasets, respectively. EfficientNetB7's exceptional performance is
credited to its innovative techniques, including compound scaling, depth-wise
separable convolutions, and squeeze-and-excitation blocks. These features allow
the model to extract more abstract and distinctive features from handwritten
text images. The study's findings hold significant implications for enhancing
identity verification and authentication systems, highlighting the potential of
deep learning in Arabic handwritten text recognition for person biometric
identification.

该研究深入探讨了深度学习模型在阿拉伯手写文本的人物生物识别上的准确性，并比较了三种先进的架构 ——ResNet50、MobileNetV2 和 EfficientNetB7，使用了三个广泛认可的数据集：AHAWP，Khatt 和 LAMIS-MSHD。结果表明，EfficientNetB7 在 AHAWP，Khatt 和 LAMIS-MSHD 数据集上的测试准确率分别达到了 98.57％，99.15％和 99.79％。EfficientNetB7 的出色性能归功于其创新的技术，包括复合缩放、深度可分离卷积和挤压和激励块。研究结果对于增强身份验证和认证系统具有重要意义，突显了深度学习在阿拉伯手写文本识别中的潜力。

基于深度学习的阿拉伯手写文本的个人生物特征识别

Arabic Handwritten Text for Person Biometric Identification: A Deep  Learning Approach

Handwriting recognition is a challenging and critical problem in the fields
of pattern recognition and machine learning, with applications spanning a wide
range of domains. In this paper, we focus on the specific issue of recognizing
offline Arabic handwritten text. Existing approaches typically utilize a
combination of convolutional neural networks for image feature extraction and
recurrent neural networks for temporal modeling, with connectionist temporal
classification used for text generation. However, these methods suffer from a
lack of parallelization due to the sequential nature of recurrent neural
networks. Furthermore, these models cannot account for linguistic rules,
necessitating the use of an external language model in the post-processing
stage to boost accuracy. To overcome these issues, we introduce two alternative
architectures, namely the Transformer Transducer and the standard
sequence-to-sequence Transformer, and compare their performance in terms of
accuracy and speed. Our approach can model language dependencies and relies
only on the attention mechanism, thereby making it more parallelizable and less
complex. We employ pre-trained Transformers for both image understanding and
language modeling. Our evaluation on the Arabic KHATT dataset demonstrates that
our proposed method outperforms the current state-of-the-art approaches for
recognizing offline Arabic handwritten text.

手写识别是模式识别和机器学习领域中具有挑战性和关键问题，其应用领域广泛。本文专注于离线阿拉伯手写文本识别问题，并引入两种替代架构，即 Transformer Transducer 和标准序列到序列 Transformer，并比较它们在准确性和速度方面的性能表现，结果显示我们的方法在识别离线阿拉伯手写文本方面优于当前最先进的方法。