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Found 21 papers, 12 papers with code
SketchGPT: Autoregressive Modeling for Sketch Generation and Recognition
We present SketchGPT, a flexible framework that employs a sequence-to-sequence autoregressive model for sketch generation, and completion, and an interpretation case study for sketch recognition.
GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding
This paper presents GeoContrastNet, a language-agnostic framework to structured document understanding (DU) by integrating a contrastive learning objective with graph attention networks (GATs), emphasizing the significant role of geometric features.
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation
Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements.
Synthetic dataset of ID and Travel Document
This paper presents a new synthetic dataset of ID and travel documents, called SIDTD.
The Common Optical Music Recognition Evaluation Framework
The quality of Optical Music Recognition (OMR) systems is a rather difficult magnitude to measure.
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance
The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions.
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes
When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system.
TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language
The field of visual document understanding has witnessed a rapid growth in emerging challenges and powerful multi-modal strategies.
Ranked #19 on
Document Image Classification
on RVL-CDIP
Beyond Document Page Classification: Design, Datasets, and Challenges
This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested ($X$: multi-channel, multi-paged, multi-industry; $Y$: class distributions and label set variety) and in classification tasks considered ($f$: multi-page document, page stream, and document bundle classification, ...).
SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation
Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image.
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation
Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc.
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts.
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis.
Ranked #5 on
Entity Linking
on FUNSD
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement.
DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer
has emerged as an interesting problem for the document analysis and understanding community.
DocEnTr: An End-to-End Document Image Enhancement Transformer
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties.
Ranked #1 on
Binarization
on H-DIBCO 2011
Graph-based Deep Generative Modelling for Document Layout Generation
One of the major prerequisites for any deep learning approach is the availability of large-scale training data.
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis
The results highlight that our model can successfully generate realistic and diverse document images with multiple objects.
Ehrhart-Equivalence, Equidecomposability, and Unimodular Equivalence of Integral Polytopes
In this paper, we establish a relationship between Ehrhart-equivalence and other forms of equivalence: the $\operatorname{GL}_n(\mathbb{Z})$-equidecomposability and unimodular equivalence of two integral $n$-polytopes in $\mathbb{R}^n$.
Combinatorics
Fault Area Detection in Leaf Diseases using k-means Clustering
With increasing population the crisis of food is getting bigger day by day. In this time of crisis, the leaf disease of crops is the biggest problem in the food industry. In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease. Leaf diseases can be detected from sample images of the leaf with the help of image processing and segmentation. Using k-means clustering and Otsu's method the faulty region in a leaf is detected which helps to determine proper course of action to be taken. Further the ratio of normal and faulty region if calculated would be able to predict if the leaf can be cured at all.
A Statistical Approach to Adult Census Income Level Prediction
The prominent inequality of wealth and income is a huge concern especially in the United States.
