Text Detection Tasks Research Articles

LoG and Structural Based Arbitrary Oriented Multilingual Text Detection in Images/Video

Text in an image or a video affords more precise meaning and text is a prominent source with a clear explanation of the content than any other high-level or low-level features. The text detection process is a still challenging research work in the field of computer vision. However, complex background and orientation of the text leads to extremely stimulating text detection tasks. Multilingual text consists of different geometrical shapes than a single language. In this article, a simple and yet effective approach is presented to detect the text from an arbitrary oriented multilingual image and video. The proposed method employs the Laplacian of Gaussian to identify the potential text information. The double line structure analysis is applied to extract the true text candidates. The proposed method is evaluated on five datasets: Hua's, arbitrarily oriented, multi-script robust reading competition (MRRC), MSRA and video datasets with performance measures precision, recall and f-measure. The proposed method is also tested on real-time video, and the result is promising and encouraging.

Scene text extraction based on edges and support vector regression

This paper presents a scene text extraction technique that automatically detects and segments texts from scene images. Three text-specific features are designed over image edges with which a set of candidate text boundaries is first detected. For each detected candidate text boundary, one or more candidate characters are then extracted by using a local threshold that is estimated based on the surrounding image pixels. The real characters and words are finally identified by a support vector regression model that is trained using bags-of-words representation. The proposed technique has been evaluated over the latest ICDAR-2013 Robust Reading Competition dataset. Experiments show that it obtains superior F-measures of 78.19 % and 75.24 % (on atom level), respectively, for the scene text detection and segmentation tasks.

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).

Visual methods for determining ambient illumination conditions when viewing medical images in mobile display devices

Abstract— The quality of the displayed image on mobile devices is affected by the varying ambient illumination conditions. Determining appropriate viewing conditions for particular visual tasks requires time and the appropriate instrumentation. To this end, the usefulness of more practical visual tests for use in clinical environments were explored. Experiments to determine the limitations of mobile displays in terms of the visibility of subtle targets for different background luminance and ambient illumination with two mobile devices were conducted. A noise‐embedded text detection task and a threshold estimation staircase technique for a range of illuminance between 1 and 80,000 lx encompassing conditions found in dark reading rooms, office spaces, and outdoor scenarios has been compared. It was found that the text detection method holds promise as a surrogate for more complicated tests in the framework of a clinically practical implementation.

24.2: Estimating the Perceptual Limits of Mobile Displays

AbstractThe quality of the displayed image in mobile devices is affected by the varying ambient illumination conditions. Determining appropriate viewing conditions for particular visual tasks requires appropriate instrumentation. However, visual tests can be more practical. We conducted experiments to explore the limitations of mobile displays in terms of the visibility of subtle targets. We explored the effect of background luminance and ambient lighting using two methodologies: a text detection task where the observer identifies characters embedded in noisy backgrounds and a threshold estimation staircase technique. We found that the text detection method holds promise as a surrogate for more complicated tests in the framework of a clinically practical implementation.