  Arabic text recognition is a challenging task because of the cursive nature
of Arabic writing system, its joint writing scheme, the large number of
ligatures and many other challenges. Deep Learning DL models achieved
significant progress in numerous domains including computer vision and sequence
modelling. This paper presents a model that can recognize Arabic text that was
printed using multiple font types including fonts that mimic Arabic handwritten
scripts. The proposed model employs a hybrid DL network that can recognize
Arabic printed text without the need for character segmentation. The model was
tested on a custom dataset comprised of over two million word samples that were
generated using 18 different Arabic font types. The objective of the testing
process was to assess the model capability in recognizing a diverse set of
Arabic fonts representing a varied cursive styles. The model achieved good
results in recognizing characters and words and it also achieved promising
results in recognizing characters when it was tested on unseen data. The
prepared model, the custom datasets and the toolkit for generating similar
datasets are made publicly available, these tools can be used to prepare models
for recognizing other font types as well as to further extend and enhance the
performance of the proposed model.
