In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions. These components are easier to handle than the entire text instance. A confidence scoring mechanism is designed to filter characters that are similar to text. Our method can integrate text contexts intensively when backgrounds are complex. Experiments on two curved challenging benchmarks demonstrate that TextCohesion outperforms state-of-the-art methods, achieving the F-measure of 84.6 on Total-Text and bfseries86.3 on SCUT-CTW1500.
Detecting text in the wild has been widely studied in the past few years. Before deep learning era, most detectors adopt Connected Components Analysis @cite_33 @cite_2 @cite_45 @cite_30 @cite_13 @cite_23 or Sliding Window based classification @cite_22 @cite_14 @cite_11 @cite_15 .
@cite_30 @cite_14 @cite_33 @cite_22 @cite_45 @cite_23 @cite_2 @cite_15 @cite_13 @cite_11
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Recognizing text in images taken in the wild is a challenging problem that has received great attention in recent years. Previous methods addressed this problem by first detecting individual characters, and then forming them into words. Such approaches often suffer from weak character detections, due to large intra-class variations, even more so than characters from scanned documents. We take a different view of the problem and present a holistic word recognition framework. In this, we first represent the scene text image and synthetic images generated from lexicon words using gradient-based features. We then recognize the text in the image by matching the scene and synthetic image features with our novel weighted Dynamic Time Warping (wDTW) approach. We perform experimental analysis on challenging public datasets, such as Street View Text and ICDAR 2003. Our proposed method significantly outperforms our earlier work in (CVPR 2012), as well as many other recent works, such as (ECCV 2012), al.(ICPR 2012), (ICCV 2011). In this paper, we present a text detection and localization method. Our detection technique is based on a cascade of boosted ensemble and localizer uses standard image processing techniques. We propose a small set of features (39 in total) capable of detecting various type of text in grey level natural scene images. Two weak learners, linear discriminant function and log likelihood-ratio test under gaussian assumption, are evaluated. Single features and combination of features are used to form weak classifiers. The proposed scheme is evaluated on ICDAR 2003 robust reading and text locating database. The results are encouraging and the detector can process an images of 640 times 480 pixels in less than 2 seconds. Detecting text in natural images is an important prerequisite. In this paper, we propose a novel text detection algorithm, which employs edge-enhanced Maximally Stable Extremal Regions as basic letter candidates. These candidates are then filtered using geometric and stroke width information to exclude non-text objects. Letters are paired to identify text lines, which are subsequently separated into words. We evaluate our system using the ICDAR competition dataset and our mobile document database. The experimental results demonstrate the excellent performance of the proposed method. Detecting text regions in natural scenes is an important part of computer vision. We propose a novel text detection algorithm that extracts six different classes features of text, and uses Modest AdaBoost with multi-scale sequential search. Experiments show that our algorithm can detect text regions with a f= 0.70, from the ICDAR 2003 datasets which include images with text of various fonts, sizes, colors, alphabets and scripts.