Open Images V5 Text Annotation and Yet Another Mask Text Spotter
A large scale human-labeled dataset plays an important role in creating high quality deep learning models. In this paper we present text annotation for Open Images V5 dataset. To our knowledge it is the largest among publicly available manually created text annotations. Having this annotation we trained a simple Mask-RCNN-based network, referred as Yet Another Mask Text Spotter (YAMTS), which achieves competitive performance or even outperforms current state-of-the-art approaches in some cases on ICDAR2013, ICDAR2015 and Total-Text datasets. Code for text spotting model available online at: https://github.com/openvinotoolkit/training_extensions. The model can be exported to OpenVINO-format and run on Intel CPUs.
Code (2)
Tasks
text annotationText SpottingSimilar Papers 제목 키워드 기반
Why You Should Try the Real Data for the Scene Text Recognition
Recent works in the text recognition area have pushed forward the recognition results to the new horizons. But for a long time a lack of large human-labeled natural text recognition datasets has been forcing researchers …
DiversityScene Text RecognitionOpen-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing works first pretrain a model on captione…
Instance SegmentationSemantic SegmentationOpen-Vocabulary Segmentation with Unpaired Mask-Text Supervision
Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is labour-intensive and poses scalability …
Language ModellingSegmentationTripletDiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models
Collecting and annotating images with pixel-wise labels is time-consuming and laborious. In contrast, synthetic data can be freely available using a generative model (e.g., DALL-E, Stable Diffusion). In this paper, we sh…
Image GenerationSemantic SegmentationMask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human effort, limiting the scalability to ann…
Image CaptioningInstance SegmentationLanguage ModelingLanguage Modelling+1