  Image captioning is a widely known problem in the area of AI. Caption
generation from floor plan images has applications in indoor path planning,
real estate, and providing architectural solutions. Several methods have been
explored in literature for generating captions or semi-structured descriptions
from floor plan images. Since only the caption is insufficient to capture
fine-grained details, researchers also proposed descriptive paragraphs from
images. However, these descriptions have a rigid structure and lack
flexibility, making it difficult to use them in real-time scenarios. This paper
offers two models, Description Synthesis from Image Cue (DSIC) and Transformer
Based Description Generation (TBDG), for the floor plan image to text
generation to fill the gaps in existing methods. These two models take
advantage of modern deep neural networks for visual feature extraction and text
generation. The difference between both models is in the way they take input
from the floor plan image. The DSIC model takes only visual features
automatically extracted by a deep neural network, while the TBDG model learns
textual captions extracted from input floor plan images with paragraphs. The
specific keywords generated in TBDG and understanding them with paragraphs make
it more robust in a general floor plan image. Experiments were carried out on a
large-scale publicly available dataset and compared with state-of-the-art
techniques to show the proposed model's superiority.
