Scene Text Detection
91 papers with code • 9 benchmarks • 15 datasets
Scene Text Detection is a computer vision task that involves automatically identifying and localizing text within natural images or videos. The goal of scene text detection is to develop algorithms that can robustly detect and and label text with bounding boxes in uncontrolled and complex environments, such as street signs, billboards, or license plates.
Source: ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection
Libraries
Use these libraries to find Scene Text Detection models and implementationsDatasets
Most implemented papers
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
TextFuseNet: Scene Text Detection with Richer Fused Features
More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.
Robust Scene Text Recognition with Automatic Rectification
We show that the model is able to recognize several types of irregular text, including perspective text and curved text.
PixelLink: Detecting Scene Text via Instance Segmentation
Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression.
Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion
By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images.
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images.
STN-OCR: A single Neural Network for Text Detection and Text Recognition
In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way.
TextBoxes++: A Single-Shot Oriented Scene Text Detector
In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass.
TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes
Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks.