  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.
