Towards Detecting, Recognizing, and Parsing the Address Information from
  Bangla Signboard: A Deep Learning-based Approach
Hasan Murad Mohammed Eunus Ali
Retrieving textual information from natural scene images is an active
research area in the field of computer vision with numerous practical
applications. Detecting text regions and extracting text from signboards is a
challenging problem due to special characteristics like reflecting lights,
uneven illumination, or shadows found in real-life natural scene images. With
the advent of deep learning-based methods, different sophisticated techniques
have been proposed for text detection and text recognition from the natural
scene. Though a significant amount of effort has been devoted to extracting
natural scene text for resourceful languages like English, little has been done
for low-resource languages like Bangla. In this research work, we have proposed
an end-to-end system with deep learning-based models for efficiently detecting,
recognizing, correcting, and parsing address information from Bangla
signboards. We have created manually annotated datasets and synthetic datasets
to train signboard detection, address text detection, address text recognition,
address text correction, and address text parser models. We have conducted a
comparative study among different CTC-based and Encoder-Decoder model
architectures for Bangla address text recognition. Moreover, we have designed a
novel address text correction model using a sequence-to-sequence
transformer-based network to improve the performance of Bangla address text
recognition model by post-correction. Finally, we have developed a Bangla
address text parser using the state-of-the-art transformer-based pre-trained
language model.