no code implementations • 30 Mar 2022 • Manu Sheoran, Meghal Dani, Monika Sharma, Lovekesh Vig
Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions.
Ranked #6 on Medical Object Detection on DeepLesion
no code implementations • 14 Mar 2022 • Arushi Jain, Shubham Paliwal, Monika Sharma, Lovekesh Vig
In this paper, we propose to train a deep network to capture the spatial associations between different word pairs present in the table image for unravelling the table structure.
no code implementations • British Machine Vision Conference 2021 • Manu Sheoran, Meghal Dani, Monika Sharma, Lovekesh Vig
In this paper, we exploit the domain information present in computed tomography (CT) scans and propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion.
Ranked #2 on Medical Object Detection on DeepLesion
no code implementations • 8 Sep 2021 • Shubham Paliwal, Arushi Jain, Monika Sharma, Lovekesh Vig
A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented in the paper.
no code implementations • 8 Sep 2021 • Shubham Paliwal, Monika Sharma, Lovekesh Vig
The proposed pipeline, named OSSR-PID, is fast and gives outstanding performance for recognition of symbols on a synthetic dataset of 100 P&ID diagrams.
5 code implementations • 6 Jan 2020 • Shubham Paliwal, Vishwanath D, Rohit Rahul, Monika Sharma, Lovekesh Vig
This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table.
Ranked #3 on Table Detection on ICDAR2013
no code implementations • 21 Nov 2019 • Monika Sharma, Shikha Gupta, Arindam Chowdhury, Lovekesh Vig
To this end, we formulate the problem of reasoning over statistical charts as a classification task using MAC-Networks to give answers from a predefined vocabulary of generic answers.
no code implementations • 14 Nov 2019 • Kushagra Mahajan, Monika Sharma, Lovekesh Vig
The algorithm is both fast and accurate and utilizes a standard Optical character recognition (OCR) engine such as Tesseract to find character based unambiguous keypoints, which are utilized to identify precise keypoint correspondences between two images.
no code implementations • 28 Jan 2019 • Rohit Rahul, Shubham Paliwal, Monika Sharma, Lovekesh Vig
To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols.
no code implementations • 28 Jan 2019 • Monika Sharma, Abhishek Verma, Lovekesh Vig
We compare the performance of CycleGAN for document cleaning tasks using unpaired images with a Conditional GAN trained on paired data from the same dataset.
no code implementations • 11 Dec 2018 • Vishwanath D, Rohit Rahul, Gunjan Sehgal, Swati, Arindam Chowdhury, Monika Sharma, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan
In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image.
Optical Character Recognition Optical Character Recognition (OCR) +2
no code implementations • 20 Oct 2018 • Monika Sharma, Tristan Glatard, Eric Gelinas, Mariam Tagmouti, Brigitte Jaumard
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers.