Stroke Width Transform Research Articles

A Shape-Aware Road Detection Method for Aerial Images

Road detection in aerial images is a crucial technique for visual navigation and scene understanding in relation to unmanned aerial vehicles (UAVs). A shape-aware road detection method for aerial images is proposed in this paper. It first employs the stroke width transform (SWT) and a geodesic distance based superpixel clustering to generate proposal regions. Then, a shape classification is responsible for selecting all potential road segments from the proposal regions which appear to be long and with consistent width. All road segments selected are clustered into several groups based on width and color features. A global graph based labeling model is then applied based on each group to remove potential background clutters, as well as to generate the final output. Experiments on two public datasets demonstrate that the proposed method can handle more diverse and challenging road scenes and needs less pre-training, leading to better performance compared to conventional methods.

Integrated Method for Text Detection in Natural Scene Images

In this paper, we present a novel image operator to extract textual information in natural scene images. First, a powerful refiner called the Stroke Color Extension, which extends the widely used Stroke Width Transform by incorporating color information of strokes, is proposed to achieve significantly enhanced performance on intra-character connection and non-character removal. Second, a character classifier is trained by using gradient features. The classifier not only eliminates non-character components but also remains a large number of characters. Third, an effective extractor called the Character Color Transform combines color information of characters and geometry features. It is used to extract potential characters which are not correctly extracted in previous steps. Fourth, a Convolutional Neural Network model is used to verify text candidates, improving the performance of text detection. The proposed technique is tested on two public datasets, i.e., ICDAR2011 dataset and ICDAR2013 dataset. The experimental results show that our approach achieves state-of-the-art performance.

RETRACTED: An Improved Fuzzy Based Algorithm for Detecting Text from Images Using Stroke Width Transform

Short Retraction Notice This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused. The full retraction notice in PDF is preceding the original paper, which is marked RETRACTED.

A Multi-stage Method for Chinese Text Detection in News Videos

With the rapid increase of on-line video resources, there is an urgent demand for text detection and recognition technologies to build content-based video indexing and retrieval systems. Chinese news video texts contain highly condensed and rich information, but the low resolution of videos on the Internet and the complexity of Chinese character structures bring challenges for text detection. In this paper, we present a multi-stage scheme for Chinese news video text detection. We propose an improved Stroke Width Transform (SWT) method by incorporating text color consistency constraint for candidate text blocks generation. Then we use “divide and conquer” strategy to distinguish candidate text blocks into three sub-spaces according to their geometric shapes and size. For each sub-space, a neural network is designed to filter the candidates into text or non-text blocks. Finally, the text blocks are merged into text lines based on the stroke width, color and other heuristic information. Experimental results on self-collected Chinese news video dataset and ICDAR 2013 dataset show that the proposed method is effective to detect both news video captions and scene texts.

Text string detection for loosely constructed characters with arbitrary orientations

Text in a scene provides vital information of its contents. With the increasing popularity of vision systems, detecting general text in images becomes a critical yet challenging task. Most existing methods have focused on extracting neatly arranged text string for compactly constructed characters. Motivated by the need to consider the widely varying forms of scene text, we propose a stroke-based text detection method which detects arbitrary orientations text strings with loosely constructed characters in images. Our approach employs result of stroke width transform (SWT) as basic stroke candidates. These candidates are then merged using adaptive structuring elements to generate compactly constructed characters. Individual characters are chained using k-nearest neighbors algorithm to identify arbitrary orientations text strings, which are subsequently separated into words if necessary. To better evaluate our system and compare it with other competing algorithms, we generate a new dataset, which includes various characters and text strings in diverse scenes. Experiments on ICDAR datasets and the proposed dataset demonstrate that our approach compares favorably with the state-of-the-art algorithms when handling arbitrary orientations text strings and achieves significantly enhanced performance on loosely constructed characters in scenes.

Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features

Vessel segmentation is an important problem in medical image analysis and is often challenging due to large variations in vessel appearance and profiles, as well as image noises. To address these challenges, we propose a solution by combining heterogeneous context-aware features with a discriminative learning framework. Our solution is characterized by three key ingredients: First, we design a hybrid feature pool containing recently invented descriptors including the stroke width transform (SWT) and Weber's local descriptors (WLD), as well as classical local features including intensity values, Gabor responses and vesselness measurements. Second, we encode context information by sampling the hybrid features from an orientation invariant local context. Third, we treat pixel-level vessel segmentation as a discriminative classification problem, and use a random forest to fuse the rich information encoded in the hybrid context-aware features. For evaluation, the proposed method is applied to retinal vessel segmentation using three publicly available benchmark datasets. On the DRIVE and STARE datasets, our approach achieves average classification accuracies of 0.9474 and 0.9633, respectively. On the high-resolution dataset HRFID, our approach achieves average classification accuracies of 0.9647, 0.9561 and 0.9634 on three different categories, respectively. Experiments are also conducted to validate the superiority of hybrid feature fusion over each individual component.

SWT voting-based color reduction method for detecting text in natural scene images

In our PhD thesis we give a very detailed and in-depth survey of natural scene text detection methods and propose two novel methods, namely SWT (Stroke Width Transform) voting-based color reduction method and SWT direction determination method. SWT voting-based color reduction method (to which we will refer also as SWT-V) is a novel text detection method that - opposed to many other text detection methods - combines both structural and color information in order to detect text. The proposed method upgrades the text detection oriented color reduction method (to which we will refer to as TOCR) with the additional SWT voting stage and substantially outperforms other state-of-the-art text detection methods. All the image colors rich with SWT pixels that most likely belong to text characters are blocked from being mean-shifted away in the color reduction process. One of the disadvantages of the SWT method, however, is the problem of ‘light text on the dark background’ described in the following sections. To cope with the problem and in order to provide true SWT values to the SWT voting stage we propose an adaptive SWT direction determination method. The method uses SWT profiles to partition an image into subblocks and analyzes their SWT histograms of both SWT search directions. Text detection literature does not explicitly address the SWT direction issue, therefore, the proposed method represents a unique scientific contribution to the research field. All text detection methods were evaluated on the CVL OCR DB text detection evaluation dataset.

EGYPTIAN LICENSE PLATE RECOGNITION USING ENHANCED STROKE WIDTH TRANSFORMATION AND FUZZY ARTMAP

License Plate Recognition (LPR) is the most importa nt type of Intelligent Transportation System (ITS). LPR is used in many different types of ITS like ele ctronic payment systems, toll station, parking fees , freeway and arterial management systems for traffic surveillance. Few years ago, Egyptians government changed the car license plate to include letters an d numbers. So the needs for efficient LPR System fo r the new license plate are increased in different ITS fi elds. This study presents an enhanced LPR detection algorithm for the new Egyptian licenses plate. The detection enhancement is done using Stroke Width Transform algorithm to extract letters from candida te areas combined with Fuzzy ARTMAP classifier. Stroke Width Transform (SWT) is a state of art algo rithm developed by Microsoft Research Lab for detecting text in natural scene, it seeks to find t he value of stroke width for each image pixel and demonstrate its use on the task of text detection i n natural images. This study is focusing on detecti ng Arabic letters in the candidate license plate area using SWT image map instead of binary image map where not all Arabic letters have uniformly stroke width and some letters have a dot above and below it. The proposed model shows 26% detection accuracy enhancement than conventional LPR systems (Sobel Edge detection with binary image map using template matching technique).

Automatic Inpainting Scheme for Video Text Detection and Removal

We present a two stage framework for automatic video text removal to detect and remove embedded video texts and fill-in their remaining regions by appropriate data. In the video text detection stage, text locations in each frame are found via an unsupervised clustering performed on the connected components produced by the stroke width transform (SWT). Since SWT needs an accurate edge map, we develop a novel edge detector which benefits from the geometric features revealed by the bandlet transform. Next, the motion patterns of the text objects of each frame are analyzed to localize video texts. The detected video text regions are removed, then the video is restored by an inpainting scheme. The proposed video inpainting approach applies spatio-temporal geometric flows extracted by bandlets to reconstruct the missing data. A 3D volume regularization algorithm, which takes advantage of bandlet bases in exploiting the anisotropic regularities, is introduced to carry out the inpainting task. The method does not need extra processes to satisfy visual consistency. The experimental results demonstrate the effectiveness of both our proposed video text detection approach and the video completion technique, and consequently the entire automatic video text removal and restoration process.

SWT voting-based color reduction for text detection in natural scene images

In this article, we propose a novel stroke width transform (SWT) voting-based color reduction method for detecting text in natural scene images. Unlike other text detection approaches that mostly rely on either text structure or color, the proposed method combines both by supervising text-oriented color reduction process with additional SWT information. SWT pixels mapped to color space vote in favor of the color they correspond to. Colors receiving high SWT vote most likely belong to text areas and are blocked from being mean-shifted away. Literature does not explicitly address SWT search direction issue; thus, we propose an adaptive sub-block method for determining correct SWT direction. Both SWT voting-based color reduction and SWT direction determination methods are evaluated on binary (text/non-text) images obtained from a challenging Computer Vision Lab optical character recognition database. SWT voting-based color reduction method outperforms the state-of-the-art text-oriented color reduction approach.

A framework for improved video text detection and recognition

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.