# Towards End-to-End Text Spotting in Natural Scenes

arXiv: https://arxiv.org/abs/1906.06013 · HTML: https://mlsanity.com/paper/1906.06013/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 19 of 19)

- [ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting](https://mlsanity.com/paper/2105.03620/) (2021)
- [SPTS v2: Single-Point Scene Text Spotting](https://mlsanity.com/paper/2301.01635/) (2023)
- [ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting](https://mlsanity.com/paper/2211.10578/) (2022)
- [GLASS: Global to Local Attention for Scene-Text Spotting](https://mlsanity.com/paper/2208.03364/) (2022)
- [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)
- [End-to-End Video Text Spotting with Transformer](https://mlsanity.com/paper/2203.10539/) (2022)
- [Layout Agnostic Scene Text Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2406.01062/) (2024)
- [Turning a CLIP Model into a Scene Text Spotter](https://mlsanity.com/paper/2308.10408/) (2023)
- [Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling](https://mlsanity.com/paper/2401.03637/) (2024)
- [DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting](https://mlsanity.com/paper/2203.05122/) (2022)
- [Zoom Text Detector](https://mlsanity.com/paper/2209.03014/) (2022)
- [Contrastive Learning of Semantic and Visual Representations for Text Tracking](https://mlsanity.com/paper/2112.14976/) (2021)
- [OCR-RTPS: An OCR-based real-time positioning system for the valet parking](https://mlsanity.com/paper/2212.04116/) (2022)
- [Weakly supervised information extraction from inscrutable handwritten document images](https://mlsanity.com/paper/2306.06823/) (2023)
- [PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer](https://mlsanity.com/paper/2308.15004/) (2023)
- [Edge Approximation Text Detector](https://mlsanity.com/paper/2504.04001/) (2025)
- [E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles](https://mlsanity.com/paper/2206.02281/) (2022)
- [Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering](https://mlsanity.com/paper/2405.12533/) (2024)
- [SilhouetteTell: Practical Video Identification Leveraging Blurred Recordings of Video Subtitles](https://mlsanity.com/paper/2510.27179/) (2025)

## Abstract (arXiv)

Text spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as image cropping and feature re-calculation, word separation, and character grouping. In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end and is able to handle text of arbitrary shapes. The convolutional features are calculated only once and shared by both detection and recognition modules. Through multi-task training, the learned features become more discriminate and improve the overall performance. By employing the 2D attention model in word recognition, the irregularity of text can be robustly addressed. It provides the spatial location for each character, which not only helps local feature extraction in word recognition, but also indicates an orientation angle to refine text localization. Our proposed method has achieved state-of-the-art performance on several standard text spotting benchmarks, including both regular and irregular ones.