# SPTS v2: Single-Point Scene Text Spotting

arXiv: https://arxiv.org/abs/2301.01635 · HTML: https://mlsanity.com/paper/2301.01635/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 82 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 30 of 56)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Pix2seq: A Language Modeling Framework for Object Detection](https://mlsanity.com/paper/2109.10852/) (2021)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network](https://mlsanity.com/paper/2002.10200/) (2020)
- [Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition](https://mlsanity.com/paper/1710.10400/) (2017)
- [ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)](https://mlsanity.com/paper/1909.07145/) (2019)
- [ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019](https://mlsanity.com/paper/1907.00945/) (2019)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [TextBoxes: A Fast Text Detector with a Single Deep Neural Network](https://mlsanity.com/paper/1611.06779/) (2016)
- [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://mlsanity.com/paper/1801.02765/) (2018)
- [TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes](https://mlsanity.com/paper/1807.01544/) (2018)
- [Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network](https://mlsanity.com/paper/1908.05900/) (2019)
- [Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting](https://mlsanity.com/paper/2007.09482/) (2020)
- [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018)
- [Detecting Text in Natural Image with Connectionist Text Proposal Network](https://mlsanity.com/paper/1609.03605/) (2016)
- [ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting](https://mlsanity.com/paper/2105.03620/) (2021)
- [Detecting Oriented Text in Natural Images by Linking Segments](https://mlsanity.com/paper/1703.06520/) (2017)

## Built on by (most cited first, 30 of 66)

- [TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document](https://mlsanity.com/paper/2403.04473/) (2024)
- [MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm](https://mlsanity.com/paper/2506.05218/) (2025)
- [OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition](https://mlsanity.com/paper/2403.19128/) (2024)
- [Attentive Eraser: Unleashing Diffusion Model's Object Removal Potential via Self-Attention Redirection Guidance](https://mlsanity.com/paper/2412.12974/) (2024)
- [TextSquare: Scaling up Text-Centric Visual Instruction Tuning](https://mlsanity.com/paper/2404.12803/) (2024)
- [Harmonizing Visual Text Comprehension and Generation](https://mlsanity.com/paper/2407.16364/) (2024)
- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [ParGo: Bridging Vision-Language with Partial and Global Views](https://mlsanity.com/paper/2408.12928/) (2024)
- [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration](https://mlsanity.com/paper/2309.01131/) (2023)
- [Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network](https://mlsanity.com/paper/2412.15678/) (2024)
- [Taylor Series-Inspired Local Structure Fitting Network for Few-shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2504.02454/) (2025)
- [ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining](https://mlsanity.com/paper/2306.12106/) (2023)
- [UPOCR: Towards Unified Pixel-Level OCR Interface](https://mlsanity.com/paper/2312.02694/) (2023)
- [GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching](https://mlsanity.com/paper/2401.07080/) (2024)
- [StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond](https://mlsanity.com/paper/2405.21013/) (2024)
- [DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training](https://mlsanity.com/paper/2408.00355/) (2024)
- [Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance](https://mlsanity.com/paper/2412.10159/) (2024)
- [Effects of Prompt Length on Domain-specific Tasks for Large Language Models](https://mlsanity.com/paper/2502.14255/) (2025)
- [ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting](https://mlsanity.com/paper/2403.00303/) (2024)
- [Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling](https://mlsanity.com/paper/2401.03637/) (2024)
- [Bridging the Gap Between End-to-End and Two-Step Text Spotting](https://mlsanity.com/paper/2404.04624/) (2024)
- [Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction](https://mlsanity.com/paper/2406.03019/) (2024)
- [InstructOCR: Instruction Boosting Scene Text Spotting](https://mlsanity.com/paper/2412.15523/) (2024)
- [SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting](https://mlsanity.com/paper/2401.07641/) (2024)
- [TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model](https://mlsanity.com/paper/2403.10047/) (2024)
- [GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking](https://mlsanity.com/paper/2505.22228/) (2025)
- [LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting](https://mlsanity.com/paper/2511.05818/) (2025)
- [CREPE: Coordinate-Aware End-to-End Document Parser](https://mlsanity.com/paper/2405.00260/) (2024)

## Abstract (arXiv)

End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. Our new framework, SPTS v2, allows us to train high-performing text-spotting models using a single-point annotation. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel, which significantly reduces the requirement of the length of the sequence. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19× faster inference speed. 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. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms. Code is available at: https://github.com/Yuliang-Liu/SPTSv2.