Handwriting recognition is an important research area in the domains of computer vision (CV), pattern recognition, and Natural Language Processing (NLP). Accurate recognition of handwritten text has numerous applications, including digitizing historical documents, automating data entry, and document analysis or verification. Handwriting recognition has been researched widely and applied to the scripts of various languages including Latin, Chinese, Hindi, and Arabic. This study focuses on offline handwritten Arabic text recognition (HATR), addressing the unique challenges posed by Arabic, which is a language spoken widely across the globe. Handwritten Arabic text poses significant challenges in handwriting recognition because of its cursive nature, rich calligraphic styles and varied diacritics. Hence, various methods have been explored to address these challenges and complexities. This paper presents a comprehensive study of the research advances in offline HATR. It includes review of the background, challenges, the strengths of methods proposed in the literature, the solutions offered, as well as highlights the weaknesses of each technique and finally, future research direction.