Search Results for author:
Found 22 papers, 7 papers with code
LAPDoc: Layout-Aware Prompting for Documents
In this paper we investigate the possibility to use purely text-based LLMs for document-specific tasks by using layout enrichment.
TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition
Table Structure Recognition is an essential part of end-to-end tabular data extraction in document images.
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets
Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency.
Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
For glaucoma classification we achieved AUC equal to 0. 874 which is 2. 7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA.
Table Structure Extraction with Bi-directional Gated Recurrent Unit Networks
Tables present summarized and structured information to the reader, which makes table structure extraction an important part of document understanding applications.
Do Cross Modal Systems Leverage Semantic Relationships?
Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space.
An Open-World Extension to Knowledge Graph Completion Models
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i. e. to predict facts for entities unseen in training based on their textual description.
Rethinking Table Recognition using Graph Neural Networks
In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition.
Converting a Common Document Scanner to a Multispectral Scanner
We propose the construction of a prototype scanner designed to capture multispectral images of documents.
Revisiting Distillation and Incremental Classifier Learning
To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature.
Distillation Techniques for Pseudo-rehearsal Based Incremental Learning
We discuss the biases in current Generative Adversarial Networks (GAN) based approaches that learn the classifier by knowledge distillation from previously trained classifiers.
A Multi-faceted OCR Framework for Artificial Urdu News Ticker Text Recognition
We present the first comprehensive data set, to our knowledge, for Urdu news ticker recognition, collected from 41 different news channels.
Real-time Document Localization in Natural Images by Recursive Application of a CNN.
We propose a document segmentation algorithm that recursively uses convolutional neural networks to precisely localize a document in a natural image.
Modeling Sub-Event Dynamics in First-Person Action Recognition
We compare our method to state-of-the-art first person and generic video recognition algorithms.
Efficient Estimation of k for the Nearest Neighbors Class of Methods
From countless experiments of the past it became widely accepted that the value of k has a significant impact on the performance of this method.
Sparseness helps: Sparsity Augmented Collaborative Representation for Classification
Many classification approaches first represent a test sample using the training samples of all the classes.
Shape-Based Automatic Detection of a Large Number of 3D Facial Landmarks
Our approach does not use texture and is completely shape based in order to detect landmarks that are morphologically significant.
Bayesian Sparse Representation for Hyperspectral Image Super Resolution
We propose a hyperspectral image super resolution approach that fuses a high resolution image with the low resolution hyperspectral image using non-parametric Bayesian sparse representation.
Discriminative Bayesian Dictionary Learning for Classification
We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data.
Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification
Efficient and accurate joint representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification.
Dense 3D Face Correspondence
A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces.
Multispectral Palmprint Encoding and Recognition
Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset).
