Text Spotting
50 papers with code • 4 benchmarks • 6 datasets
Text Spotting is the combination of Scene Text Detection and Scene Text Recognition in an end-to-end manner. It is the ability to read natural text in the wild.
Libraries
Use these libraries to find Text Spotting models and implementationsDatasets
Latest papers with no code
TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model
Taking advantage of the fine-tuned language model on scene recognition benchmarks and the paradigm of text block detection, extensive experiments demonstrate the superior performance of our scene text spotter across multiple public benchmarks.
Efficiently Leveraging Linguistic Priors for Scene Text Spotting
This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive scene text spotting and recognition models.
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision.
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.
Watermark Text Pattern Spotting in Document Images
Watermark text spotting in document images can offer access to an often unexplored source of information, providing crucial evidence about a record's scope, audience and sometimes even authenticity.
Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling
Specifically, we propose an innovative reading-order estimation module (REM) that extracts reading-order information from the initial text boundary generated by an initial boundary module (IBM).
Word length-aware text spotting: Enhancing detection and recognition in dense text image
Scene text spotting is essential in various computer vision applications, enabling extracting and interpreting textual information from images.
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.
Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration
We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including document analysis, retrieval, and office automation.
Deformation Robust Text Spotting with Geometric Prior
Based on this database, we develop a deformation robust text spotting method (DR TextSpotter) to solve the recognition problem of complex deformation of characters in different fonts.