  In this paper, we propose SUGAMAN (Supervised and Unified framework using
Grammar and Annotation Model for Access and Navigation). SUGAMAN is a Hindi
word meaning "easy passage from one place to another". SUGAMAN synthesizes
textual description from a given floor plan image for the visually impaired. A
visually impaired person can navigate in an indoor environment using the
textual description generated by SUGAMAN. With the help of a text reader
software, the target user can understand the rooms within the building and
arrangement of furniture to navigate. SUGAMAN is the first framework for
describing a floor plan and giving direction for obstacle-free movement within
a building. We learn $5$ classes of room categories from $1355$ room image
samples under a supervised learning paradigm. These learned annotations are fed
into a description synthesis framework to yield a holistic description of a
floor plan image. We demonstrate the performance of various supervised
classifiers on room learning. We also provide a comparative analysis of system
generated and human written descriptions. SUGAMAN gives state of the art
performance on challenging, real-world floor plan images. This work can be
applied to areas like understanding floor plans of historical monuments,
stability analysis of buildings, and retrieval.
