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Found 13 papers, 9 papers with code
OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest recently.
Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design
By integrating this deep learning model with a specific sampling strategy in the latent space, a novel, microstructure-centered algorithm for multiphase alloy design is developed.
Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid
To address this issue, we introduce a Complementary Image Pyramid (CIP), a simple, effective, and plug-and-play solution designed to mitigate semantic discontinuity during high-resolution image processing.
VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization
Typically, we propose a Prompt Queries Generation Module and a Tasks-aware Adapter to effectively convert the original single-task model into a multi-task model suitable for both image and video scenarios with minimal additional parameters.
Bridging the Gap Between End-to-End and Two-Step Text Spotting
Subsequently, we introduce a Bridge that connects the locked detector and recognizer through a zero-initialized neural network.
SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting
In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter v2, which seeks to find a better synergy between text detection and recognition.
Progressive Evolution from Single-Point to Polygon for Scene Text
We demonstrate the accuracy of the generated polygons through extensive experiments: 1) By creating polygons from ground truth points, we achieved an accuracy of 82. 0% on ICDAR 2015; 2) In training detectors with polygons generated by our method, we attained 86% of the accuracy relative to training with ground truth (GT); 3) Additionally, the proposed Point2Polygon can be seamlessly integrated to empower single-point spotters to generate polygons.
Hierarchical Side-Tuning for Vision Transformers
In this paper, we introduce Hierarchical Side-Tuning (HST), an innovative PETL method facilitating the transfer of ViT models to diverse downstream tasks.
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder.
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
In this paper, we conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks including Text Recognition, Scene Text-Centric Visual Question Answering (VQA), Document-Oriented VQA, Key Information Extraction (KIE), and Handwritten Mathematical Expression Recognition (HMER).
SPTS v2: Single-Point Scene Text Spotting
Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations.
Ranked #15 on
Text Spotting
on ICDAR 2015
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition.
Ranked #3 on
Text Spotting
on Inverse-Text
SPTS: Single-Point Text Spotting
For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance.
Ranked #3 on
Text Spotting
on SCUT-CTW1500
