CVL OCR DB, an annotated image database of texts in natural scenes, and its usability

Text detection and optical character recognition (OCR) in images of natural scenes is a fairly new computer vision area but yet very useful in numerous applicative areas. Although many implementations gain promising results, they are evaluated mostly on the private image collections that are very hard or even impossible to get. Therefore, it is very difficult to compare them objectively. Since our aim is to help the research community in standardizing the evaluation of the text detection and OCR methods, we present CVL OCR DB, a public database of annotated images of text in diverse natural scenes, captured at varying weather and lighting conditions. All the images in the database are annotated with the text region and single character location information, making CVL OCR DB suitable for testing and evaluating both text detection and OCR methods. Moreover, all the single characters are also cropped from the original images and stored individually, turning our database into a huge collection of characters suitable for training and testing OCR classifiers

An improved edge profile based method for text detection in images of natural scenes

Text detection in natural images has gained much attention in the last years as it is a primary step towards fully autonomous text recognition. Understanding the visual text content is of a vital importance in many applicative areas from the internet search engines to the PDA signboard translators. Images of natural scenes, however, pose numerous difficulties compared to the traditional scanned documents. They mainly contain diverse complex text of different sizes, styles and colors with complex backgrounds. Furthermore, such images are captured under variable lighting conditions and are often affected by the skew distortion and perspective projections. In this article an improved edge profile based text detection method is presented. It uses a set of heuristic rules to eliminate detection of non-text areas. The method is evaluated on CVL OCR DB, an annotated image database of text in natural scenes

CVL OCR DB, an annotated image database of texts in natural scenes, and its usability

Text detection and optical character recognition (OCR) in images of natural scenes is a fairly new computer vision area but yet very useful in numerous applicative areas. Although many implementations gain promising results, they are evaluated mostly on the private image collections that are very hard or even impossible to get. Therefore, it is very difficult to compare them objectively. Since our aim is to help the research community in standardizing the evaluation of the text detection and OCR methods, we present CVL OCR DB, a public database of annotated images of text in diverse natural scenes, captured at varying weather and lighting conditions. All the images in the database are annotated with the text region and single character location information, making CVL OCR DB suitable for testing and evaluating both text detection and OCR methods. Moreover, all the single characters are also cropped from the original images and stored individually, turning our database into a huge collection of characters suitable for training and testing OCR classifiers

Grupiranje teksta v slikah naravnih scen

Avtomatična razpoznava teksta v slikah naravnih scen postaja v zadnjih letih zelo popularna – tako na raziskovalnem kot na aplikativnem področju. Da bi bila razpoznava teksta čimbolj natančna, je potrebno predhodno tekst v sliki pravilno detektirati ter ga ustrezno segmentirati v posamezne vrstice in besede. Pravilna segmentacija je namreč predpogoj za dobro delovanje same razpoznave teksta. V članku opisujemo metodo za grupiranje črk detektiranih v slikah naravnih scen v posamezne vrstice. Metoda temelji na izgradnji minimalnega vpetega drevesa, katerega vozlišča so posamezne detektirane črke, ter iskanju optimalnih poddreves, ki ustrezajo posameznim vrsticam v tekstu. Iskanje optimalnih poddreves je optimizacijski problem, ki temelji na minimizaciji skupne energije vseh poddreves. Metoda je evalvirana na zbirki slik teksta v naravnih scenah CVL OCR DB

Text detection methods in images of natural scenes

Text detection in natural scene images has gained much attention in the last years due to its enormous applicative potential in many areas such as content-based image retrieval, PDA signboard translators and applications for assisting blind and visually impaired people. A clear distinction, however, has to be made between text detection and text recognition. The task of the former is to locate text regions in an image, not to recognize them. Nevertheless, text detection and text recognition are closely related since the detected text regions can be subsequently fed into the text recognition modules. Due to diversity and complexity of natural scene images, the text detection task is considerably challenging. Text can appear at arbitrary image locations in arbitrary shapes, sizes and colors. Additionally, it is often subject to numerous geometric transformations. Finally, natural scene images contain very complex backgrounds, which make text detection even more difficult. In this dissertation, we present two novel methods: SWT voting-based color reduction and SWT direction determination. The first is a text detection-oriented segmentation method, that supervises the color reduction process by integrating additional SWT information. It improves segmentation accuracy compared to the other state-of-the-art methods. Colors rich with SWT pixels most likely belong to text and are therefore blocked from being mean-shifted away towards background colors. One of the disadvantages of the SWT method is the search direction problem. The method searches for parallel character edges in the gradient directions. In case of a dark text on a light background gradients correctly point towards character interiors, whereas in case of a light text on a dark background they point in the opposite directions and cause incorrect text detection. In order to solve the problem, the authors of the SWT method run the algorithm twice - in gradient and counter-gradient directions. Such approach, however, is imprecise and time consuming since the whole method has to be run twice. To avoid the search direction issue, we present a novel SWT direction determination method. By analyzing SWT sub-block histograms of both gradient and counter-gradient directions, the method is able to determine the correct SWT direction in one step. Usually, ICDAR 2003 and ICDAR 2011 datasets are used for text detection evaluation. Their disadvantage is rectangular annotation of single words in images, which requires, that the detected text is already grouped into words. Since text detection and word grouping are separate subjects, such annotation is problematic from the perspective of objective evaluation. Therefore, we created our own public annotated dataset of text in natural scene images CVL OCR DB. The dataset supports two types of annotation: n-polygon annotation and binary annotation. The latter allows per character evaluation and makes word grouping unnecessary. Experimental results on the CVL OCR DB dataset indicate that the SWT voting-based color reduction method outperforms the text-oriented color reduction method, which is used in the segmentation phase of the state-of-the-art text detection method of structure-based partition and grouping. Literature does not explicitly address SWT search direction issue; thus, the SWT direction determination method cannot be directly compared to the other methods. Nevertheless, the method achieves high detection rate on the CVL OCR DB dataset and is able to determine correct SWT directions when both dark text on light backgrounds and light text on dark backgrounds appear in the image. Generally speaking, the dissertation can also serve as a survey of text detection in natural scene images

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

Advisor: Peter Peer. Date and location of PhD thesis defense: 24 October 2013, University of LjubljanaIn 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

Automatic computerized measurement of retinal blood vessels with adaptive tracking algorithm and association with blood pressure

Background: To validate an automatic computer-based method for measuring the caliber of retinal blood vessels and use it to determine the effects of arterial hypertension on the calibers of these vessels and on their ratio.Methods: 295 patients with increased blood pressure were analyzed. All arterioles and venules located in the area between one half and one disc diameter from the optic disc margin were measured with the computer based program. These measurements were combined to provide the average diameters of retinal arterioles and venules and the association with blood pressure was analyzed. The arteriole-to-venule ratio (AVR) was also calculated.Results: The average arteriolar diameter of patients who had hypertension from 5 to 15 years was 89.311 μm. Patients with hypertension for more than 15 years they had value of 79.276 μm. Average venular diameters were very similar in both groups (103.319 μm vs. 101.392 μm). We noticed differences in average arteriolar diameter between control group and hypertonic patients who had hypertension for more than 15 years (92.083 μm vs. 79.276 μm). Venular differences were minimal. The average of retinal venules in control group was 106.029 μm, in patients with hypertension for more than 15 years it was 101.392 μm.Conclusions: Using a computer-assisted method to measure retinal vessel diameter we found out that the diameter of retinal arterioles narrowed with blood pressure level. Our findings demonstrate a relation between presence and severity of hypertension and retinal diameter. Diameter of retinal venules hardly changed. Such relationship was similar with men and women. Fully automated system for analyzing retinal vessels is simple to use, quick and reliable.</p