105 research outputs found
Towards Detecting, Recognizing, and Parsing the Address Information from Bangla Signboard: A Deep Learning-based Approach
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
A Hierarchical Approach for Investigating Social Features of a City from Mobile Phone Call Detail Records
Cellphone service-providers continuously collect Call Detail Records (CDR) as
a usage log containing spatio-temporal traces of phone users. We proposed a
multi-layered hierarchical analytical model for large spatio-temporal datasets
and applied that for the progressive exploration of social features of a city,
e.g., social activities, relationships, and groups, from CDR. This approach
utilizes CDR as the preliminary input for the initial layer, and analytical
results from consecutive layers are added to the knowledge-base to be used in
the subsequent layers to explore more detailed social features. Each subsequent
layer uses the results from previous layers, facilitating the discovery of more
in-depth social features not predictable in a single-layered approach using
only raw CDR. This model starts with exploring aggregated overviews of the
social features and gradually focuses on comprehensive details of social
relationships and groups, which facilitates a novel approach for investigating
CDR datasets for the progressive exploration of social features in a
densely-populated city
Understanding Social Structures from Contemporary Literary Fiction using Character Interaction Graph -- Half Century Chronology of Influential Bengali Writers
Social structures and real-world incidents often influence contemporary
literary fiction. Existing research in literary fiction analysis explains these
real-world phenomena through the manual critical analysis of stories.
Conventional Natural Language Processing (NLP) methodologies, including
sentiment analysis, narrative summarization, and topic modeling, have
demonstrated substantial efficacy in analyzing and identifying similarities
within fictional works. However, the intricate dynamics of character
interactions within fiction necessitate a more nuanced approach that
incorporates visualization techniques. Character interaction graphs (or
networks) emerge as a highly suitable means for visualization and information
retrieval from the realm of fiction. Therefore, we leverage character
interaction graphs with NLP-derived features to explore a diverse spectrum of
societal inquiries about contemporary culture's impact on the landscape of
literary fiction. Our study involves constructing character interaction graphs
from fiction, extracting relevant graph features, and exploiting these features
to resolve various real-life queries. Experimental evaluation of influential
Bengali fiction over half a century demonstrates that character interaction
graphs can be highly effective in specific assessments and information
retrieval from literary fiction. Our data and codebase are available at
https://cutt.ly/fbMgGEMComment: 8 pages, 11 figures, 6 pages appendi
The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis
Analyzing the writing styles of authors and articles is a key to supporting
various literary analyses such as author attribution and genre detection. Over
the years, rich sets of features that include stylometry, bag-of-words, n-grams
have been widely used to perform such analysis. However, the effectiveness of
these features largely depends on the linguistic aspects of a particular
language and datasets specific characteristics. Consequently, techniques based
on these feature sets cannot give desired results across domains. In this
paper, we propose a novel Word2vec graph based modeling of a document that can
rightly capture both context and style of the document. By using these Word2vec
graph based features, we perform classification to perform author attribution
and genre detection tasks. Our detailed experimental study with a comprehensive
set of literary writings shows the effectiveness of this method over
traditional feature based approaches. Our code and data are publicly available
at https://cutt.ly/svLjSgkComment: 12 pages, 6 figure
Assessment of the outcomes of open side-to-side choledochoduodenostomy in the management of choledocholithiasis
Background: Gallstone disease is one of the most common digestive diseases leading to frequent hospital visits and its prevalence shows ethnic variability, with rates of approximately 10-15% in the United States and Europe. The present study aims to prospectively assess the outcomes of open side-to-side choledochoduodenostomy in the management of choledocholithiasis.
Methods: This hospital-based prospective observational study was conducted in the Department of Surgery, Tezpur medical College and Hospital, Tezpur, over one year period, from July 2021 to June 2022. The study includes twenty-four patients admitted to the surgery department for bile duct stone operations. After intraoperative confirmation of the criteria, these patients underwent choledochoduodenostomy. The patients were followed for 2 months postoperatively after discharge.
Results: Only a few patients had immediate postoperative complications which were managed conservatively. No patient had any evidence of retained stone, nor did they have any symptoms of cholangitis, features suggestive of the development of Sump syndrome, or any other follow-up postoperative complications.
Conclusion: Open side-to-side choledochoduodenostomy should be considered a method of choice in remote areas where endoscopic facilities are lacking and in patients where cost is a factor in deciding the choice of procedure, with reduced postoperative complications like retained stones and a shorter duration of hospital stay in expert surgical hands
- …