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Text Detection and Recognition: A Review

Text Detection and Recognition: A Review

stages in the process of text detection and recognition and analyses different approaches used for text extraction from color images. Two commonly used methods for this problem are stepwise methods and integrated methods, whereas this task is further divided into text detection and localization, classification, segmentation and text recognition. Important approaches used to undergo these stages and their corresponding advantages, disadvantages and applications are presented in this paper. Various text related applications for imagery are also presented over here. This review performs comparative analysis of fundamental processes in this field.
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Text-detection and -recognition from natural images

Text-detection and -recognition from natural images

proposed to fill the gap of character-level annotation, the number of samples in the exists datasets and the availability of text in different orientations. Most of the available datasets suffer from the lack of samples for training, especially those used for deep learning approaches. Moreover, the existed dataset lack diversity and variation texts, most of datasets are specialized in one orientation of texts such as horizontal, multi orientation or curve. They are annotated in the line or word level; character level annotation is not available especially for detection task. The proposed dataset was created particularly for deep learning methods which require a massive completed and various range of training data. The dataset contributed in this study includes 38,500 images of English characters and 12,500 words in more than 2100 images. It is contained text in an arbitrary shape (combination of horizontal, multi-oriented, irregular and curved text). The proposed dataset annotated by myself in character level and word level. I believe this is the first dataset that produces digit annotation along with character annotation, while most of the existing datasets ignore digit annotation. Furthermore, my other contribution is the proposed of augmentation tool which is created to support the proposed dataset due to the missing of the augmentation tool for object detection tasks. The position of the bounding boxes needs to be updated for object detection augmentation. Therefore, this study provided an augmentation tool along with the proposed dataset for bounding boxes augmentation without the requirement for annotating new images, the position of the bounding boxes and the class can be obtained automatically from the original image. This technique helped to increase the number of samples in the dataset and reduce the annotation time, no annotation was required. 3- A new robust method for text detection and recognition to overcome the limitation of
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Segmentation Framework for Multi-Oriented Text Detection and Recognition

Segmentation Framework for Multi-Oriented Text Detection and Recognition

________________________________________________________________________________________________________ I. INTRODUCTION Text detection and extraction method play an important role in many applications over segmentation. It is challenging task due to rapidly increase the digitization of all the materials. It is complex because we need to find out where the text located in the scene image. Text extraction process involves text detection, segmentation and recognition of text. Scene images contains the text, such as the advertising boards, banners, vehicle number plates, street sign boards and traffic sign board etc, which is captured by cameras. This text is difficult to detect and recognized due to their various styles, font, color, contrast and orientation.
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AUTOMATED TEXT DETECTION AND RECOGNITION FROM COMPLEX IMAGES

AUTOMATED TEXT DETECTION AND RECOGNITION FROM COMPLEX IMAGES

Keywords: DWT(Discrete wavelet transform), Text detection Text extraction, Text recognition,. I. INTRODUCTION Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rapid advancement in the technology and multimedia has digitalized the world. The availability of cameras and other systems contributes large number of images to the world. Ranging from cameras embedded in mobile phones to professional ones, Surveillance cameras to broadcast videos, every day images to satellite images, all these contributes to increase in multi-media data. Most of the images may contain text as part of it, which gives some information about that image. Therefore identification of these texts has relevance in many applications. This shows the importance of the text extraction system in lot of applications. It was stated that in recent years there was a drastic in-crease in multimedia libraries and the amount of data is growing exponentially with time. Generally, the images can be categorized into three based on its type:
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Deep learning for text detection and recognition in complex engineering diagrams.

Deep learning for text detection and recognition in complex engineering diagrams.

Abstract—Engineering drawings such as Piping and Instru- mentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams.
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Survey on Image Text Detection and Recognition of Natural Scene Images

Survey on Image Text Detection and Recognition of Natural Scene Images

III. CONCLUSION Text is born out of different types of illumination factors such as color, size, shape, effects, lighting etc. All these factors are seen in image background. The rich and exact information is embodied in text, which can assist a wide range of real-world applications. Therefore, the detecting and recognizing text in natural scenes have been recognized as important research areas in computer vision. Most of the existing systems are concentrated with text in English and some other languages like Chinese, Korean. Only the English language text is widely considered research field. It is prominent for developing the detection and the recognition systems which have capacity to handle texts of di erent languages also. This literature review is aimed at tracing the recent advancements in scene text detection and recognition. Table 1 describes the comparisons made from the survey which is helpful to identify new methods that extracts the textual information from natural scenes accurately and robustly.
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Chinese Text Detection and Recognition in Natural Scene Using HOG and SVM

Chinese Text Detection and Recognition in Natural Scene Using HOG and SVM

Currently there are generally two types of methods for text location, texture-based method and region-based method. Texture-based method retrieves images’ texture features through a sliding window and predicts the probabilities for different windows to contain characters. For such methods, their strength is the robustness on resolving image noise, whereas their weakness is the significantly increased complexity in computation after the incorporation of sliding windows. Moreover, as this method cannot be used to retrieve split characters, it is not advantageous for character recognition. By using AdaBoost classifier, Chen[2] combined mean gray value and variance characteristics of an image’s partial areas to locate text region. Through the optimized Niblack algorithm[3], they performed binarization on the image and obtained the character candidate regions. Pan et al. [4] proposed to integrate features of HOG and multi-scale Local Binary Pattern for text detection, and then use Markov Random Field to merge characters into words. This method earned the best score in ICDAR 2005 Text Detection Contest. Yet its disadvantage is the enormous computation power and time required in execution. Lee[5] et al. improved the efficiency of text detection through using six different features to represent characters. In this method, multi-scale continuous search was combined with a certain level of AdaBoost algorithm to achieve a strong classifier
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A Survey on Real Time Text Detection and Recognition from Traffic Panels

A Survey on Real Time Text Detection and Recognition from Traffic Panels

Keywords— Text detection, Text recognition, Maximally stable extremal regions (MSERs), Optical Character recognition (OCR) I. INTRODUCTION Each government imposes some sets of rules and regulations to ensure a safe traffic system. Each person specially the vehicle driver must obey these rules and regulations for a secure travel. Some of those laws are represented as visual language such as different signs and texts that are known as traffic signs. There are various categories of traffic signs that we can see beside the roads. An efficient driver must notice each of the road signs in front of him and need to act accordingly. Otherwise disastrous things can happen. A driver may not notice each of the road signs in front of his car due to lack of care or human perception errors. As shown in Fig. 1 there is a huge Percentage of road accidents in India. Therefore, it is desirable of having a automatic road sign detection and recognition system to assist the driver to ensure a safe travel.
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Rough-fuzzy based scene categorization for text detection and recognition in video

Rough-fuzzy based scene categorization for text detection and recognition in video

Recipes Cooking Teleshopping Yoga Craft Making Indian Classical Music Concert Figure 17: Samples of video frames of new 5 classes with text detection by [6]. are reported in Table 13 and Table 14, respectively. With the same parameter setup, the results reported in Table 13 and Table 14 show that the text detection and recognition performance of the text detection and binarization methods improves significantly after classification compared those of prior to classification with the similar conclusion we have drawn for the data of 10 classes. In the same way, most of the text detection methods and binarization methods give better results for the proposed classification compared to existing classification. Furthermore, it is noted from the results of 10 classes and 5 new classes, the detection and recognition rates report almost similar patterns after classification. In summary, from the above experimental analysis, one can confirm that the proposed classification has the ability to extend to a number of new classes, and the performance of classification is independent from the content of frames in terms of text detection and recognition rates. Hence, the proposed method is generic and consistent to different classes or different contents of frames. This is because of the use of flexible rough-fuzzy combination, covariance-correlation for intra, inter planes and temporal information. Since our aim is to show the effectiveness of the classification method, we tune parameters of the text detection and binarization methods. As a result, the improved results after classification is not high as plain document analysis accuracy which usually has more than 90% accuracy. However, this work shows direction that one can modify the existing methods or develop new methods according to the complexity of individual classes for achieving still better performance of text detection and recognition by considering the advantage of classification.
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Text Detection And Recognition From The Captions Of Streaming Videos Using Tracking

Text Detection And Recognition From The Captions Of Streaming Videos Using Tracking

8. C ONCLUSION AND F UTURE S COPE The both detecting and Recognizing text in the video shares a few difficulties practically speaking, for example, robustness to background complexity, text degradation and distortion, content variations, and moving objects. The text background is regularly intricate, particularly for scene message and embedded caption text. A few parts of the background can be fundamentally the same as the text and text objects are normally little, the two of which lessen the accuracy of text detection and tracking. Pre-preparing the image aides, and text recognition is effective with improved accuracy. Likewise, the east text detector should be broke down and altered further to improve its text detection yield, for it to be utilized in progressively practical and complex applications. As the extraction text is stored in this work, this can be further extended in the automation system. The program and algorithm be fed to automated devices which can be giving intuitive services to illiterates, disabled as well as they process the data faster and can convert it as speech, presentation etc. and serve as digital video analyzer assistants.
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Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT

Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT

Keywords: K-means clustering, model-based image, OCR, text detection, SIFT and scene image. Abstract: Natural urban scene images contain many problems for character recognition such as luminance noise, varying font styles or cluttered backgrounds. Detecting and recognizing text in a natural scene is a difficult problem. Several techniques have been proposed to overcome these problems. These are, however, usually based on a bottom-up scheme, which provides a lot of false positives, false negatives and intensive computation. There- fore, an alternative, efficient, character-based expectancy-driven method is needed. This paper presents a modeling approach that is usable for expectancy-driven techniques based on the well-known SIFT algorithm. The produced models (Object Attention Patches) are evaluated in terms of their individual provisory character recognition performance. Subsequently, the trained patch models are used in preliminary experiments on text detection in scene images. The results show that our proposed model-based approach can be applied for a coherent SIFT-based text detection and recognition process.
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Text detection and recognition from images 
		as an aid to blind persons accessing unfamiliar environments

Text detection and recognition from images as an aid to blind persons accessing unfamiliar environments

Independent travel is a well known challenge for blind or visually impaired persons. The text reading algorithm has proved to be robust in many kinds of real-world scenarios, including indoor and outdoor places with a wide variety of text appearance due to different writing styles, fonts, colors, sizes, textures and layouts, as well as the presence of geometrical distortions, partial occlusions, and different shooting angles that may cause deformed text. In this paper, we propose a method to detect panels and to recognize the information inside them. The proposal extracts local descriptors at some interest key points after applying color segmentation. Then, images are represented as a bag of visual words (BOVW) and classified using support vector machines. Finally, text detection and recognition method is applied on those images where a panel has been detected, in order to automatically read and save the information depicted in the panels. A language model partly based on a dynamic dictionary is also used.
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Review on Text Detection, Extraction and Recognition from Images and Videos

Review on Text Detection, Extraction and Recognition from Images and Videos

KEYWORDS : Text Detection, Text extraction and Text Recognition. I. INTRODUCTION Text is born as an explicit carrier of high level semantics. This unique property makes text different from other generic visual cues, such as contour, color and texture. Therefore, detecting and recognizing texts in natural scenes have become important and vibrant research areas in computer vision. Text extraction is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents (text).The problem is challenging in nature due to variations in text properties and reflections. Text appearing in images is classified into three categories: document text, caption text, and scene text [6]. In contrast to caption text, scene text can have any orientation and may be distorted by the perspective projection therefore it is more difficult to detect scene text.
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Survey on Text Detection, Segmentation and Recognition from a Natural Scene Images

Survey on Text Detection, Segmentation and Recognition from a Natural Scene Images

Detecting and recognizing text from natural scene image is more difficult task than all other types of images. It have various affecting factors like light effects, orientation, font styles, blur, etc. Even though there are many algorithms, no single unified approach can fits for all the applications. So there is lot of scope to work with the text detection, extraction, segmentation and recognition from natural scene images. Also there is scope for detecting text from various languages, which have different characteristics than English.
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Text Detection and Recognition in Natural Images

Text Detection and Recognition in Natural Images

© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 998 Optical character Recognition (OCR) is a conversion of scanned or printed text images, handwritten text into editable text for further processing. This technology allows machine to recognize the text automatically. It is like combination of eye and mind of human body. An eye can view the text from the images but actually the brain processes as well as interprets that extracted text read by eye. In development of computerized OCR system, few problems can occur. First: there is very little visible difference between some letters and digits for computers to understand. For example it might be difficult for the computer to differentiate between digit “0” and letter “o”.
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Text Detection and Recognition from Natural Scene

Text Detection and Recognition from Natural Scene

applications such that mobile text recognition, scene understanding, automatic recognition sign board, supports for visually impaired persons, license plate detection, robot navigation, extract traffic sign board text that uses for intelligent transport system, navigational support for tourist guide, information retrieval etc..These are the variety of application which is develop with the use of mobile phone because mobile phone captured the scene text and directly convert into the recognition process. So, when we perform any text recognition, it is very important to extract the text region accurately. Text extraction in natural scene image use for so many applications, but still it is challenging task due complexity of its complex background, color variation, noise problem, image illumination changes, image distortion, blurring problem and lighting condition [2]. To extract the text from the LED display is not an easy task, it is very complex due to its discontinuity.The text which is superimposed into an image contains a useful text which represents the whole image information. Text is mainly classified into two categories:
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Detection and Recognition of Text for Dusty Image using Long Short Term Memory

Detection and Recognition of Text for Dusty Image using Long Short Term Memory

3. CONCLUSIONS The solution for standard challenges in scene text analysis for dusty image is proposed. The problem of text detection and recognition for dusty images is address which can be used for many applications such as content- based image retrieval, sign translation and navigation aid for the visually impaired and robots, driverless car. The various detection and recognition of text techniques are reviewed. The effort of researchers, considerable progress had made in natural scene image detection and recognition of text in recent past years. Aiming at the problems that text regions in dusty images always exit some problem such as blur, text features weakened or lost, low definition, low resolution low contrast. There is need of dusty image is enhanced by enhancement algorithm and then text are detected and recognized in the enhanced image. The text and non-text regions are divided by the maximally stable extremal regions. The geometric property is used to remove non-text regions from images which greatly reduces the computational cost. Then text are detected and recognized in these images by using Long Short Term Memory (LSTM). As an overview of the results of the previous chapters, It can be conclude that 81.87%
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DETECTION AND RECOGNITION OF THE TEXT IN IMAGE USING CC CLUSTERING AND  NON TEXT FILTERING

DETECTION AND RECOGNITION OF THE TEXT IN IMAGE USING CC CLUSTERING AND NON TEXT FILTERING

Fig.4.A sub network for the modular neural network Multi-layer perceptron is trained for the classification of square patches, use one hidden layer consisting of 20 nodes and set the output value to +1 for text samples and 0 otherwise. To help the learning, input features are normalized. Multilayer perceptron is a neural network used to compare the input image with the already trained texts then if the texts are matched then the given input text is detected as the output. Through the multilayer perceptron we can select as required input text for the comparison. Already trained some font styles are present in multilayer perceptron. The performance measure used for text detection, which
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WordFences: Text localization and recognition

WordFences: Text localization and recognition

1.1. Motivation Detection and recognition of text in natural images has long been an outstanding chal- lenge in the computer vision and machine learning communities. Text recognition in the wild can provide context and semantic information for scene understanding, object clas- sification and action recognition in images or video. The task has attracted interest of many researchers [5, 12, 11, 32, 31, 44, 7]. Due to the difficulty of text detection in nat- ural images, even state-of-the-art systems struggle with word localization because of the staggering variety of text sizes and fonts, potentially poor image quality, low contrast, image distortions, or presence of patterns visually similar to text such as signs, icons or textures. Many works in text detection employ knowledge-based algorithms and heuris- tics in order to tackle these challenges. Some of the most common techniques include:
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An Efficient Industrial System for Vehicle Tyre (Tire) Detection and Text Recognition Using Deep Learning

An Efficient Industrial System for Vehicle Tyre (Tire) Detection and Text Recognition Using Deep Learning

In this paper, we presented a complete pipeline for detecting and reading tyre codes of a moving vehicle using roadside cameras. The article also presented a novel technique for efficient proposal generation by combining HOG with CNN based classifier. Using state-of-the-art deep learning models and fully convolutional networks, a robust and efficient archi- tecture was presented. Although, in the given problem, there is no benchmark to compare the performance against, the image results show that it is quite effective and accurate. There is still room for further improvement, especially in the text detector. Making it robust to both weak characters as well as for closely spaced fonts will improve the over all accuracy of the system. Other aspects for further investigation are multi- scale text detection tied to a bounding box regressor and a separate date classifier within an end-to-end framework than in a cascade.
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