Text Detection Method Research Articles

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.

Multioriented Video Scene Text Detection Through Bayesian Classification and Boundary Growing

Multioriented text detection in video frames is not as easy as detection of captions or graphics or overlaid texts, which usually appears in the horizontal direction and has high contrast compared to its background. Multioriented text generally refers to scene text that makes text detection more challenging and interesting due to unfavorable characteristics of scene text. Therefore, conventional text detection methods may not give good results for multioriented scene text detection. Hence, in this paper, we present a new enhancement method that includes the product of Laplacian and Sobel operations to enhance text pixels in videos. To classify true text pixels, we propose a Bayesian classifier without assuming a priori probability about the input frame but estimating it based on three probable matrices. Three different ways of clustering are performed on the output of the enhancement method to obtain the three probable matrices. Text candidates are obtained by intersecting the output of the Bayesian classifier with the Canny edge map of the input frame. A boundary growing method is introduced to traverse the multioriented scene text lines using text candidates. The boundary growing method works based on the concept of nearest neighbors. The robustness of the method has been tested on a variety of datasets that include our own created data (nonhorizontal and horizontal text data) and two publicly available data, namely, video frames of Hua and complex scene text data of ICDAR 2003 competition (camera images). Experimental results show that the performance of the proposed method is encouraging compared with results of existing methods in terms of recall, precision, F-measures, and computational times.

Video text detection and localization in intra-frames of H.264/AVC compressed video

Video texts are closely related to the video content. The video text information can facilitate content based video analysis, indexing and retrieval. Video sequences are usually compressed before storage and transmission. A basic step of text-based applications is text detection and localization. In this paper, an overlaid text detection and localization method is proposed for H.264/AVC compressed videos by using the integer discrete cosine transform (DCT) coefficients of intra-frames. The main contributions of this paper are in the following two aspects: 1) coarse text blocks detection using block sizes and quantization parameters adaptive thresholds; 2) text line localization according to the characteristics of text in intra frames of H.264/AVC compressed domain. Comparisons are made with the pixel domain based text detection method for the H.264/AVC compressed video. Text detection results on five H.264/AVC video sequences under various qualities show the effectiveness of the proposed method.

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.

A Novel Video Image Text Detection Method

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

A Robust Multilingual Text Detection Approach Based on Transforms and Wavelet Entropy

In the present work, we describe the study and performance of a robust text detection method in color and regular images. The system employs two important stages: Firstly the combination of wavelet transforms and gabor filter is applied to extract sharpened edges and textural features of a given input image. In the second stage, wavelet entropy is imposed to get an energy value of a resulted gabor image in order to find the high frequency texture elements of a processed one to determine the true text region of an image. Experiments are conducted on standard ICDAR database and with multi-lingual dataset. Experimental results show that the proposed method is able to detect text of different font size, complex background and contrast. Furthermore, the system performance surpasses the existing method in terms of detection accuracy.

An Edge-Based Algorithm for Text Extraction in Images and Video Frame

In this paper, a proposal of a new and unusual framework to detect and extract the text from the images and video frames have been presented. In the past various methods have been presented for detection and localization of text in images and video frames. In this paper, a comparison has been made between several text detection methods and proposed method for text detection in images and video frames. The proposed method is carried out by edge detection, and the projection profile method is used to localize the text region better. Various experiments have been carried out to evaluate and compare the performance of the proposed algorithm. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical. Various parameters like average time, precision and recall rates and analyzed for both existing and proposed method to determine the success and limitation of our method.

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.

A novel mutual nearest neighbor based symmetry for text frame classification in video

In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.

Text detection and restoration in natural scene images

A new method for text detection and recognition in natural scene images is presented in this paper. In the detection process, color, texture, and OCR statistic features are combined in a coarse-to-fine framework to discriminate texts from non-text patterns. In this approach, color feature is used to group text pixels into candidate text lines. Texture feature is used to capture the “dense intensity variance” property of text pattern. Statistic features from OCR (Optical Character Reader) results are employed to further reduce detection false alarms empirically. After the detection process, a restoration process is used. This process is based on plane-to-plane homography. It is carried out to refine the background plane of text when an affine transformation is detected on a located text and independent of camera parameters. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical.

Text detection, localization, and tracking in compressed video

Video text information plays an important role in semantic-based video analysis, indexing and retrieval. Video texts are closely related to the content of a video. Usually, the fundamental steps of text-based video analysis, browsing and retrieval consist of video text detection, localization, tracking, segmentation and recognition. Video sequences are commonly stored in compressed formats where MPEG coding techniques are often adopted. In this paper, a unified framework for text detection, localization, and tracking in compressed videos using the discrete cosines transform (DCT) coefficients is proposed. A coarse to fine text detection method is used to find text blocks in terms of the block DCT texture intensity information. The DCT texture intensity of an 8×8 block of an intra-frame is approximately represented by seven AC coefficients. The candidate text block regions are further verified and refined. The text block region localization and tracking are carried out by virtue of the horizontal and vertical block texture intensity projection profiles. The appearing and disappearing frames of each text line are determined by the text tracking. The final experimental results show the effectiveness of the proposed methods.

A learning-based method to detect and segment text from scene images

This paper proposes a learning-based method for text detection and text segmentation in natural scene images. First, the input image is decomposed into multiple connected-components (CCs) by Niblack clustering algorithm. Then all the CCs including text CCs and non-text CCs are verified on their text features by a 2-stage classification module, where most non-text CCs are discarded by an attentional cascade classifier and remaining CCs are further verified by an SVM. All the accepted CCs are output to result in text only binary image. Experiments with many images in different scenes showed satisfactory performance of our proposed method.

카메라 획득 영상에서의 색 분산 및 개선된 K-means 색 병합을 이용한 텍스트 영역 추출 및 이진화

이미지에 포함된 텍스트는 이미지의 내용을 함축적이고 구체적으로 표현하는 정보로서 이러한 정보를 실시간에 찾아내서 인식한다면 다양한 응용에 활용할 수 있다. 본 논문에서는 카메라로 취득한 다양한 종류의 이미지로부터 텍스트를 추출하는 방법과 추출된 영역에서 텍스트를 분리하는 방법을 새롭게 제안한다. 텍스트 영역 추출을 위해서 RGB 색 공간에서 색 분산을 특징으로 제안하며, 텍스트 영역 분리를 위해서 RGB 색 공간에서 개선된 K-means 병합을 제안한다. 실험은 디지털 카메라와 핸드폰 카메라로 취득한 다양한 종류의 문서유형 이미지와 실내외의 일반적인 자연이미지를 사용하였으며, ICDAR 콘테스트[1] 이미지의 일부도 사용하였다. Texts in images have significant and detailed information about the scenes, and if we can automatically detect and recognize those texts in real-time, it can be used in various applications. In this paper, we propose a new text detection method that can find texts from the various camera-captured images and propose a text segmentation method from the detected text regions. The detection method proposes color variance as a detection feature in RGB color space, and the segmentation method suggests an improved K-means color clustering in RGB color space. We have tested the proposed methods using various kinds of document style and natural scene images captured by digital cameras and mobile-phone camera, and we also tested the method with a portion of ICDAR[1] contest images.

A comprehensive method for multilingual video text detection, localization, and extraction

Text in video is a very compact and accurate clue for video indexing and summarization. Most video text detection and extraction methods hold assumptions on text color, background contrast, and font style. Moreover, few methods can handle multilingual text well since different languages may have quite different appearances. This paper performs a detailed analysis of multilingual text characteristics, including English and Chinese. Based on the analysis, we propose a comprehensive, efficient video text detection, localization, and extraction method, which emphasizes the multilingual capability over the whole processing. The proposed method is also robust to various background complexities and text appearances. The text detection is carried out by edge detection, local thresholding, and hysteresis edge recovery. The coarse-to-fine localization scheme is then performed to identify text regions accurately. The text extraction consists of adaptive thresholding, dam point labeling, and inward filling. Experimental results on a large number of video images and comparisons with other methods are reported in detail.

An Automatic Performance Evaluation Protocol for Video Text Detection Algorithms

Text presented in videos provides important supplemental information for video indexing and retrieval. Many efforts have been made for text detection in videos. However, there is still a lack of performance evaluation protocols for video text detection. In this paper, we propose an objective and comprehensive performance evaluation protocol for video text detection algorithms. The protocol includes a positive set and a negative set of indices at the textbox level, which evaluate the detection quality in terms of both location accuracy and fragmentation of the detected textboxes. In the protocol, we assign a detection difficulty (DD) level to each ground truth textbox. The performance indices can then be normalized with respect to the textbox DD level and are therefore tolerant to different ground-truth difficulties to a certain degree. We also assign a detectability index (DI) value to each ground-truth textbox. The overall detection rate is the DI-weighted average of the detection qualities of all ground-truth textboxes, which makes the detection rate more accurate to reveal the real performance. The automatic performance evaluation scheme has been applied to performance evaluation of a text detection approach to determine the best thresholds that can yield the best detection results. The protocol has also been employed to compare the performances of several text detection systems. Hence, we believe that the proposed protocol can be used to compare the performance of different video/image text detection algorithms/systems and can even help improve, select, and design new text detection methods.

Efficient Automatic Text Location Method and Content-Based Indexing and Structuring of Video Database

An efficient automatic text detection and location method for video documents is proposed and its application for the content-based retrieval of video is presented and discussed. Target frames are selected at fixed time intervals from shots detected by a scene-change detection method. For each selected frame, segmentation by color clustering is performed around color peaks using a color histogram. For each color plane, text-lines are detected using heuristics, and the temporal and spatial position and the text-image of each text-line are stored in a database. Experimental results for text detection in video images and the performance of the method are reported for various video documents. A user interface for text-image based browsing is designed for direct content-based access to video documents, and other applications are discussed.