Search Results for author:
Found 12 papers, 1 papers with code
An Efficient Anchor-free Universal Lesion Detection in CT-scans
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
DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection
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
TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words
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.
Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams
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.
OSSR-PID: One-Shot Symbol Recognition in P&ID Sheets using Path Sampling and GCN
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.
TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images
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
ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks
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.
Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction
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.
Automatic Information Extraction from Piping and Instrumentation Diagrams
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.
Learning to Clean: A GAN Perspective
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.
Deep Reader: Information extraction from Document images via relation extraction and Natural Language
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
Data models for service failure prediction in supply-chain networks
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers.
