  The main aim of this study is the assessment and discussion of a model for
hand-written Arabic through segmentation. The framework is proposed based on
three steps: pre-processing, segmentation, and evaluation. In the
pre-processing step, morphological operators are applied for Connecting Gaps
(CGs) in written words. Gaps happen when pen lifting-off during writing,
scanning documents, or while converting images to binary type. In the
segmentation step, first removed the small diacritics then bounded a connected
component to segment offline words. Huge data was utilized in the proposed
model for applying a variety of handwriting styles so that to be more
compatible with real-life applications. Consequently, on the automatic
evaluation stage, selected randomly 1,131 images from the IESK-ArDB database,
and then segmented into sub-words. After small gaps been connected, the model
performance evaluation had been reached 88% against the standard ground truth
of the database. The proposed model achieved the highest accuracy when compared
with the related works.
