Camera-based scene images usually have complex background filled with non-text objects in multiple shapes and colors. The existing system is sensitive to font scale changes and background interference. The main focusof this system is on two character recognition methods. In text detection, previously proposed algorithms are used to search for regions of text strings. Proposed system uses character descriptor which is effective to extract representative and discriminative text features for both recognition schemes. The local features descriptor HOG is compatible with all above key point detectors. Our method of scene text recognition from detected text regions is compatible with the application of mobile devices. Proposedsystem accurately extracts text from natural scene image in presence of background interference.The demo system gives us details of algorithm design and performance improvements of scene text extraction. It is ableto detect text region of text strings from cluttered and recognize characters in the text regions.
See Full PDF See Full PDFText detection and recognition in natural scene can give valuable information for many applications. However, getting text from images with complex background is challenging task due to less frequency of occurrence text and presence of background outliers resembling text characters. In text detection, algorithms from previous work are applied to localize text region in scene image. First, character descriptor is employed to extract structure features. Second, we tend to designed novel feature representation, stroke configuration map using character boundary and skeleton to build character structure. Our algorithm style is improved to compatible with mobile application. Developed algorithm style is compatible with the appliance of scene text extraction in good mobile devices. The Android-based demo system is developed to highlight the effectiveness of the method of scene text extraction from nearby objects. Also demo system gives the detailed information about algorithm design and pe.
Download Free PDF View PDF
Download Free PDF View PDF
Abstract—In modern technology, availability of high resolution camera has lead to new dimension in digital image processing. As the technology is being expanding, various technologies are being developed for mobile devices. The goal of our project is to recognize and extract the text from images captured by camera based mobile device, and once the text is recognised information about the text can be obtain via Dictionary or via Web.
Download Free PDF View PDF
The increase in availability of high performance, low-priced, portable digital imaging devices has created an opportunity for supplementing traditional scanning for document image acquisition. Cameras attached to cellular phones, wearable computers, and standalone image or video devices are highly mobile and easy to use; they can capture images making them much more versatile than desktop scanners. Should gain solutions to the analysis of documents captured with such devices become available, there will clearly be a demand in many domains. Images captured from images can suffer from low resolution, perspective distortion, and blur, as well as a complex layout and interaction of the content and background. In this paper, we present a survey of application domains and technical challenges for the analysis of documents captured by digital cameras. Each method is discussed in brief and then compared against other approaches.
Download Free PDF View PDF
The key problems which gain the attention of many researchers in recent years is increasing popularity of portable devices for capturing images, visual processing, text extraction, etc. Extraction of text information from images or scene involves detection, localization, tracking, segmentation, enhancement and character recognition. But because of variations involved in text such as font style, size, orientation, alignment, reflections and illumination effect, with complex background and low image contrast make text extraction process more challenging. A large number of methods have been proposed to address this problem but still none of them are perfect. This paper presents an effective approach for text detection and recognition in an image.
Download Free PDF View PDF
International Journal for Research in Applied Science and Engineering Technology IJRASET
The text localization and recognition in real time scene text images has become a significant issue in the current scenario. Mobile application and digitization has given a significant and broad impact on real time scene text images. However, the accuracy of recognition rate is based upon the text localization, i.e., higher the clarity of text background segmentation and decomposition, higher the speed of accuracy for the image recognition. In this project, we present a brand new scene text detection algorithm which supports Stroke detection and Hog Transform method. The tactic introduces an approach for character detection and recognition which integrates the benefits of Feature extraction techniques and distribution of Connected Components. Characters are detected and recognized on the image regions and then the image is segmented, then each segment is transformed into a set of connected strokes. The tactic was evaluated on a standard dataset consisting mostly real time images where it achieves advanced results in both text localization and recognition. The results clearly depict the higher accuracy in terms of localization and recognition for real time images.
Download Free PDF View PDF
Detecting text regions in natural scene images has become an important area due to its varies applications. Text Information Extraction (TIE) System involves detecting text regions in a given image, localizing it, extracting the text part and recognizing text using OCR. This work basically concentrates on the detection and extraction of text in natural scene images. In this work, the test image will be pre- processed using RGB to Gray conversion, binarization, Edge Detection method and Geometric based Noise removal method. The features from the pre-processed image are extracted. The extracted features are used by the trained SVM classifier to detect the text regions. After detecting text regions, characters are extracted and finally displayed.
Download Free PDF View PDF
Detecting text from an image is an important prerequisite for the content based image analysis process. To understand the contents of an image or the valuable information, there is need of analyzing the text appears in it. Various methods have been proposed over past years for text detection and extraction from different types of images, like scene image, born digital image and document image. In this paper, we describe the existing methods of text detection, text segmentation and character recognition from natural scene images with their features, advantages and disadvantages.
Download Free PDF View PDF
International Journal of Engineering Research and Technology (IJERT)
https://www.ijert.org/mobile-camera-based-text-detection-and-translation https://www.ijert.org/research/mobile-camera-based-text-detection-and-translation-IJERTV3IS11034.pdf Text in a natural image directly carry rich high-level semantic information about a scene, which can be used to assist a wide variety of applications, such as image understanding, image indexing and search,geolocation or navigation, and human computer interaction. However, most existing text detection and recognition systems are designed for horizontal or near-horizontal texts. Witt the increasingly popular computing-on-the-go devices, detecting texts of arbitrary orientations from images taken by such devices under less controlled conditions has become an increasingly important and yet challenging task. In this project, we are using a new algorithm to detect texts of arbitrary orientations in natural images. Our algorithm is based on a two-level classification scheme and utilize two sets of features specially designed for capturing both intrinsic and orientation-invariant characteristics of texts. To better evaluate the proposed method and compare it with other existing algorithms, we generate a more extensive and challenging dataset, which includes various types of texts in diverse real-world scenes.
Download Free PDF View PDF
International Journal of Signal and Imaging Systems Engineering
