Automatic text extraction and character segmentation using maximally stable extremal regions (original) (raw)
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Text Extraction from Natural Images Using Maximally Stable Extremal Regions
Detecting text in natural images is an important prerequisite. A large number of techniques have been proposed to address this problem and the purpose of this paper is to classify and review these techniques. In this paper a novel text detection algorithm as connected component based text detection algorithm is used for detection and recognition of text from the natural images using edge enhanced Maximally Stable Extremal Regions and stroke Width transform. Basic idea is to extract the connected component with MSER algorithms and then these candidates are filtered using CC based analysis and stroke width filtering. The text detected is then recognized using optical character recognizer.
Maximally Stable Extremal Region Approach for Accurate Text Detection in Natural Scene Images
Text detection method discovers the presence of text in images, videos, etc. This technique is very needful for many applications, based on content based image analysis, such as web image search, map analysis, video information retrieval, etc. It is very challenging to detect the text from natural scene images due to its complex background and noise. The objective is to design a text detection system which will be able to detect maximum characters from natural scene images. In this text detection system, first, the input natural scene image is pre-processed. The input color image is converted into grey and then Otsu binarization algorithm is applied on grey scale image. Then, in the next stage, character candidates are extracted from binarized image using the MSERs region detector algorithm. MSER will extract various features from input image and then divide it into number of different regions. Then, morphological filter is applied to remove noise and unwanted regions detected by MSER. After morphological operation some heuristic rules are applied to these regions; which removes non-text candidates. The probability of text is estimated by calculating features like height, width and area of contours. Finally, text candidates corresponding to true texts are constructed and displayed using rectangle.
A Comprehensive Study on Character Segmentation
2019
In identifying the characters from a given image, character segmentation plays an important role. In a given line of text, first, we have to segment the words. Then, in each word there will be a character-by-character segmentation. There have been some rapid developments in this area. Many algorithms have been implemented to increase the accuracy range and decrease the word error rate. This paper aims to provide a review of some of the developments that have happened in this domain.
Journal of Computer Science, 2020
Extracting texts from images with complex backgrounds is a major challenge today. Many existing Optical Character Recognition (OCR) systems could not handle this problem. As reported in the literature, some existing methods that can handle the problem still encounter major difficulties with extracting texts from images with sharp varying contours, touching word and skewed words from scanned documents and images with such complex backgrounds. There is, therefore, a need for new methods that could easily and efficiently extract texts from these images with complex backgrounds, which is the primary reason for this work. This study collected image data and investigated the processes involved in image processing and the techniques applied for data segmentation. It employed an adaptive thresholding algorithm to the selected images to properly segment text characters from the image's complex background. It then used Tesseract, a machine learning product, to extract the text from the image file. The images used were coloured images sourced from the internet with different formats like jpg, png, webp and different resolutions. A custom adaptive algorithm was applied to the images to unify their complex backgrounds. This algorithm leveraged on the Gaussian thresholding algorithm. The algorithm differs from the conventional Gaussian algorithm as it dynamically generated the blocksize to apply threshing to the image. This ensured that, unlike conventional image segmentation, images were processed area-wise (in pixels) as specified by the algorithm at each instance. The system was implemented using Python 3.6 programming language. Experimentation involved fifty different images with complex backgrounds. The results showed that the system was able to extract English character-based texts from images with complex backgrounds with 69.7% word-level accuracy and 81.9% character-level accuracy. The proposed method in this study proved to be more efficient as it outperformed the existing methods in terms of the character level percentage accuracy.
An Automated Text Extraction System for Complex Images
The automatic text extraction system involves intelligent algorithms to identify and extract the textual content present in various kinds of images. With the advent of the digital era and the availability of myriad of multimedia contents, it has become extremely important to read and interpret the texts associated with those contents. The automatic extraction of texts would not only serve to infer the semantics of those multimedia documents but also help in effi cient indexing and subsequent retrieval of the same. However, the text differs in size, style, alignment etc. and low resolution of the background of complex images make the problem of text identifi cation a complex one. Hence, the extraction of text data in images has become a challenging fi eld of research in the domain of Image Processing. The main limitation of the existing techniques such as texture-based or connected-component based is that they are unable to provide accurate results with great precision for the applications of text extraction. The proposed Text Extraction System would intelligently read the text regions from various complex images. The design includes various stages like localization, segmentation and fi nally recognition of the textual data in images. For the localization of text, Discrete Wavelength Transform function is used. Then the morphological operations are applied to correctly mark the text regions. After that, the text portion is segmented and recognized by an effi cient system. A big advantage of this system is that the output which is a text data can be stored in a .txt fi le format. Furthermore, modifi cation of the extracted text is also possible. This proposed approach can be used in more advanced and sophisticated applications as it has exhibited better precision rate, effi ciency and recall rate.
Text Extraction System by Eliminating Non-Text Regions
IJCSIS Vol 17 No 5 May Issue, 2019
Text detection and recognition in scene images or natural images has applications in computer vision systems like registration number plate detection, automatic traffic sign detection, image retrieval and help for visually impaired people. Scene text, however, has complicated background, blur image, partly occluded text, variations in font-styles, image noise and ranging illumination. Hence scene text recognition could be a difficult computer vision problem. In this paper connected component method is used to extract the text from background. In this work, horizontal and vertical projection profiles, geometric properties of text, image binirization and gap filling method are used to extract the text from scene images. Then histogram based threshold is applied to separate text background of the images. Finally text is extracted from images.
International Journal of Innovative Research in Engineering & Management
Text extraction is one of the key tasks in document image analysis. Automatic text extraction without characters recognition capabilities is to extract regions just contains text. The text extraction process includes detection, localization, segmentation and enhancement of the text from the given input image. In this paper we present a comparative study and performance evaluation of various text extraction techniques.
Image Segmentation for Text Extraction
This paper presents a methodology for extracting text from images such as document images, scene images etc. Text that appears in these images contains important and useful information. Text extraction in images has been used in large variety of applications such as mobile robot navigation, document retrieving, object identification, vehicle license plate detection, etc. In this paper, we employ discrete wavelet transform (DWT) for extracting text information from complex images. The input image may be a colour image or a grayscale image. If the image is colour image, then preprocessing is required. For extracting text edges, the sobel edge detector is applied on each subimage. The resultant edges so obtained are used to form an edge map. Morphological operations are applied on the processed edge map and further thresholding is applied to o improve the performance.
A New Approach to Detect and Extract Characters from Off-Line Printed Images and Text
Procedia Computer Science, 2013
Characters extraction is the most critical pre-processing step for any off-line text recognition system because the characters are the smallest unit of any language script. The paper proposes an approach to segment character images from the text containing images and computer printed or handwritten words. This segmentation app roach is based on a set of properties for each connected component (object) in the whole binary image of the machine printed or handwritten text containing some other images. These words which are printed along with some images are of different lengths and are printed by different cursive fonts of different sizes. This character extraction technique is applied for the segmentation of untouched characters from the machine printed or handwritten words of varying length written on a noisy background having some images etc. Very promising results are achieved which reveals the robustness of the proposed character detection and extraction technique.
TEXT EXTRACTION USING MULTIPLE THERSHOLD ALGORITHM
High color similarity between text pixels and background pixels is the major problem that causes failure during text localization. In this paper, a novel algorithm, Reverse Thresholds (RT) algorithm is proposed to localize text from the images with various text-background color similarities. First, a rough calculation is proposed to determine the similarity index for every text region. Then, by applying reverse operation, the best thresholds for each text region are calculated by its similarity index. To remove other uncertainties, self-generated images with the same text features but different similarity index are used as experiment dataset. Experiment result shows that RT algorithm has higher localizing strength which is able to localize text in a wider range of similarity index.