  Slide presentations are an effective and efficient tool used by the teaching
community for classroom communication. However, this teaching model can be
challenging for blind and visually impaired (VI) students. The VI student
required personal human assistance for understand the presented slide. This
shortcoming motivates us to design a Classroom Slide Narration System (CSNS)
that generates audio descriptions corresponding to the slide content. This
problem poses as an image-to-markup language generation task. The initial step
is to extract logical regions such as title, text, equation, figure, and table
from the slide image. In the classroom slide images, the logical regions are
distributed based on the location of the image. To utilize the location of the
logical regions for slide image segmentation, we propose the architecture,
Classroom Slide Segmentation Network (CSSN). The unique attributes of this
architecture differs from most other semantic segmentation networks. Publicly
available benchmark datasets such as WiSe and SPaSe are used to validate the
performance of our segmentation architecture. We obtained 9.54 segmentation
accuracy improvement in WiSe dataset. We extract content (information) from the
slide using four well-established modules such as optical character recognition
(OCR), figure classification, equation description, and table structure
recognizer. With this information, we build a Classroom Slide Narration System
(CSNS) to help VI students understand the slide content. The users have given
better feedback on the quality output of the proposed CSNS in comparison to
existing systems like Facebooks Automatic Alt-Text (AAT) and Tesseract.
