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Found 12 papers, 6 papers with code
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions.
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions.
P2RBox: A Single Point is All You Need for Oriented Object Detection
In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module.
Turning a CLIP Model into a Scene Text Spotter
Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26. 5% and 5. 5% for text detection and spotting tasks, respectively.
Looking and Listening: Audio Guided Text Recognition
Text recognition in the wild is a long-standing problem in computer vision.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
ICDAR 2023 Competition on Reading the Seal Title
To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2).
Turning a CLIP Model into a Scene Text Detector
Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection.
ADASYN-Random Forest Based Intrusion Detection Model
Through the comparative experiment of Intrusion detection on CICIDS 2017 dataset, it is found that ADASYN with Random Forest performs better.
Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation
Specifically, our approach consists of two key modules, a conditional domain discriminator~(CDD) and a category-centric prototype aligner~(CCPA).
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently.
MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture.
