Text recognition technology in landscape images has become a major area of concern, as the assistive technology industry has used it as an important and crucial tool to serve as a basis for developing discoveries and making major advances in innovative assistive technology solu- tions. Assistive technology has been developed for individuals with different types of disabilities through the use of text recognition in landscape images. These solutions enabled persons with disabilities to be active members of society in various fields such as education, employment and others. Recognizing text in landscape photos is a practical but challenging task due to the large differences in backgrounds, textures, fonts, lighting variable, occlusions, variable orientations, and a immense number of non-text objects in nature which has a form similar to textual ele- ments. The Arabic cursive script poses extra challenges that need further investigation. Text extraction technology is based on pattern recognition as text detection, text recognition, and script identification is required. This paper discusses this ICDAR 2017 MLT Arab dataset and OCR methodology that surpassed the challenges that faced the Arabic text environment af- fected by different font sizes, font styles, image resolution, and opacity of text. Our aim in this project, to present a new solution that enables rapid test detection in landscape images, which takes advantage of developments in machine learning and patterns to be a useful reference for researchers and developers.