An end-to-end, segmentation-free, deep learning model trained from scratch is
proposed, leveraging DCNN for feature extraction, alongside Bidirectional
Long-Short Term Memory (BLSTM) for sequence recognition and Connectionist
Temporal Classification (CTC) loss function on the KHATT database. The training
phase yields remarkable results 84% recognition rate on the test dataset at the
character level and 71% on the word level, establishing an image-based sequence
recognition framework that operates without segmentation only at the line
level. The analysis and preprocessing of the KFUPM Handwritten Arabic TexT
(KHATT) database are also presented. Finally, advanced image processing
techniques, including filtering, transformation, and line segmentation are
implemented. The importance of this work is highlighted by its wide-ranging
applications. Including digitizing, documentation, archiving, and text
translation in fields such as banking. Moreover, AHR serves as a pivotal tool
for making images searchable, enhancing information retrieval capabilities, and
enabling effortless editing. This functionality significantly reduces the time
and effort required for tasks such as Arabic data organization and
manipulation.

提出了一种从头开始训练的、端到端的、无分割的深度学习模型，利用 DCNN 进行特征提取，同时利用双向 LSTM 进行序列识别和 CTC 损失函数在 KHATT 数据库上进行训练，使得字符级别上测试数据集的识别率达到 84%，单词级别上达到 71%，建立了一种仅在行级别上操作而无需分割的基于图像的序列识别框架。同时介绍了 KFUPM Handwritten Arabic TexT (KHATT) 数据库的分析和预处理。最后，实现了包括滤波、变换和行分割等高级图像处理技术。该工作的重要性在于其广泛的应用，包括银行业的数字化、文档化、存档和文本翻译。此外，AHR 作为一种关键工具，可以使图像可搜索，增强信息检索能力，并实现轻松编辑。这一功能显著减少了阿拉伯语数据组织和处理等任务所需的时间和努力。