BCE-Arabic-v1 dataset: Towards interpreting Arabic document images for people with visual impairments

Millions of individuals in the Arab world have significant visual impairments that make it difficult for them to access printed text. Assistive technologies such as scanners and screen readers often fail to turn text into speech because optical character recognition software (OCR) has difficulty to interpret the textual content of Arabic documents. In this paper, we show that the inaccessibility of scanned PDF documents is in large part due to the failure of the OCR engine to understand the layout of an Arabic document. Arabic document layout analysis (DLA) is therefore an urgent research topic, motivated by the goal to provide assistive technology that serves people with visual impairments. We announce the launching of a large annotated dataset of Arabic document images, called BCE-Arabic-v1, to be used as a benchmark for DLA, OCR and text-to-speech research. Our dataset contains 1,833 images of pages scanned from 180 books and represents a variety of page content and layout, in particular, Arabic text in various fonts and sizes, photographs, tables, diagrams, and charts in single or multiple columns. We report the results of a formative study that investigated the performance of state-of-the-art document annotation tools. We found significant differences and limitations in the functionality and labeling speed of these tools, and selected the best-performing tool for annotating our benchmark BCE-Arabic-v1.