Search Results for author:
Found 15 papers, 2 papers with code
Understanding Video Scenes through Text: Insights from Text-based Video Question Answering
Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively.
Reading Between the Lanes: Text VideoQA on the Road
Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness.
Watching the News: Towards VideoQA Models that can Read
We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods.
An empirical study of CTC based models for OCR of Indian languages
Recognition of text on word or line images, without the need for sub-word segmentation has become the mainstream of research and development of text recognition for Indian languages.
ICDAR 2021 Competition on Document VisualQuestion Answering
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges.
Asking questions on handwritten document collections
This work addresses the problem of Question Answering (QA) on handwritten document collections.
InfographicVQA
Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements.
Benchmarking Scene Text Recognition in Devanagari, Telugu and Malayalam
And the performance is bench-marked on a new IIIT-ILST dataset comprising of hundreds of real scene images containing text in the above mentioned scripts.
MMBERT: Multimodal BERT Pretraining for Improved Medical VQA
Images in the medical domain are fundamentally different from the general domain images.
Document Visual Question Answering Challenge 2020
For the task 1 a new dataset is introduced comprising 50, 000 questions-answer(s) pairs defined over 12, 767 document images.
DocVQA: A Dataset for VQA on Document Images
The dataset consists of 50, 000 questions defined on 12, 000+ document images.
Ranked #1 on
Visual Question Answering (VQA)
on DocVQA val
RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets.
ICDAR 2019 Competition on Scene Text Visual Question Answering
ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image.
A Cost Efficient Approach to Correct OCR Errors in Large Document Collections
Word error rate of an ocr is often higher than its character error rate.
Unconstrained Scene Text and Video Text Recognition for Arabic Script
For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data.
