Recent Trends and Techniques in Text Detection and Text Localization in a Natural Scene: A Survey

Text information extraction from natural scene images is a rising area of research. Since text in natural scene images generally carries valuable details, detecting and recognizing scene text has been deemed essential for a variety of advanced computer vision applications. There has been a lot of effort put into extracting text regions from scene text images in an effective and reliable manner. As most text recognition applications have high demand of robust algorithms for detecting and localizing texts from a given scene text image, so the researchers mainly focus on the two important stages text detection and text localization. This paper provides a review of various techniques of text detection and text localization

Aggregated Text Transformer for Scene Text Detection

This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. Starting from the image pyramid with multiple resolutions, the features are first extracted at different scales with shared weight and then fed into an encoder-decoder architecture of Transformer. The multi-scale image representations are robust and contain rich information on text contents of various sizes. The text Transformer aggregates these features to learn the interaction across different scales and improve text representation. The proposed method detects scene texts by representing each text instance as an individual binary mask, which is tolerant of curve texts and regions with dense instances. Extensive experiments on public scene text detection datasets demonstrate the effectiveness of the proposed framework

A New Approach for Text String Detection from Natural Scenes By Grouping & Partition

In this paper we have reviewed and analyzed different methods to find strings of characters from natural scene images. We have reviewed different techniques like extraction of character string regions from scenery images based on contours and thickness of characters, efficient binarization and enhancement technique followed by a suitable connected component analysis procedure, text string detection from natural scenes by structure - based partition and grouping, and a robust algorithm for text detection in images. It is assumed that characters have closed contours, and a character string consists of characters which lie on a straight line in most cases. Therefore, by extracting closed contours and searching neighbors of them, character string regions can be extracted; Image binarization successfully processed natural scene images having shadows, non - uniform illumination, low contrast and large signal - dependent noise. Connected component analysis is used to define the final binary images that mainly consist of text regions. One technique chooses the candidate text characters from connected components by gradient feature and color feature. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitte d text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region coveri ng all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi - scales. The proposed methods outperform the state - of - the - art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in no horizontal orientations

Text Extraction From Natural Scene: Methodology And Application

With the popularity of the Internet and the smart mobile device, there is an increasing demand for the techniques and applications of image/video-based analytics and information retrieval. Most of these applications can benefit from text information extraction in natural scene. However, scene text extraction is a challenging problem to be solved, due to cluttered background of natural scene and multiple patterns of scene text itself. To solve these problems, this dissertation proposes a framework of scene text extraction. Scene text extraction in our framework is divided into two components, detection and recognition. Scene text detection is to find out the regions containing text from camera captured images/videos. Text layout analysis based on gradient and color analysis is performed to extract candidates of text strings from cluttered background in natural scene. Then text structural analysis is performed to design effective text structural features for distinguishing text from non-text outliers among the candidates of text strings. Scene text recognition is to transform image-based text in detected regions into readable text codes. The most basic and significant step in text recognition is scene text character (STC) prediction, which is multi-class classification among a set of text character categories. We design robust and discriminative feature representations for STC structure, by integrating multiple feature descriptors, coding/pooling schemes, and learning models. Experimental results in benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, which obtains better performance than previously published methods. Our proposed scene text extraction framework is applied to 4 scenarios, 1) reading print labels in grocery package for hand-held object recognition; 2) combining with car detection to localize license plate in camera captured natural scene image; 3) reading indicative signage for assistant navigation in indoor environments; and 4) combining with object tracking to perform scene text extraction in video-based natural scene. The proposed prototype systems and associated evaluation results show that our framework is able to solve the challenges in real applications

An Efficient and Robust Method for Text Detection in Low and High Resolution Natural Scene Images

In this paper, we propose efficient and sturdy technique for investigating texts in natural scene footage. A fast and effective pruning formula is designed to extract Maximally Stable External Regions (MSERs) as character candidate’s victimization the strategy of minimizing regularized variations. Character candidates form into text candidates by the single-link clump formula, wherever distance weights and clump threshold unit of measurement learned by a completely distinctive self-training distance metric learning formula. The probabilities of text candidates like non-text unit of measurement estimable with a temperament classifier. Text candidates with high non-text probabilities unit of density eliminated and texts unit of measurement acknowledged with a document classifier. Text find in natural scene footage is also an important for several content-based image resolve. Experiments on polyglot, street browse; multi-direction and even born-digital databases conjointly demonstrate the effectiveness of the reposed technique. DOI: 10.17762/ijritcc2321-8169.15078

Text Detection Using Transformation Scaling Extension Algorithm in Natural Scene Images

In recent study efforts, the importance of text identification and recognition in images of natural scenes has been stressed more and more. Natural scene text contains an enormous amount of useful semantic data that can be applied in a variety of vision-related applications. The detection of shape-robust text confronts two major challenges: 1. A large number of traditional quadrangular bounding box-based detectors failed to identify text with irregular forms, making it difficult to include such text within perfect rectangles.2. Pixel-wise segmentation-based detectors sometimes struggle to identify closely positioned text examples from one another. Understanding the surroundings and extracting information from images of natural scenes depends heavily on the ability to detect and recognise text. Scene text can be aligned in a variety of ways, including vertical, curved, random, and horizontal alignments. This paper has created a novel method, the Transformation Scaling Extention Algorithm (TSEA), for text detection using a mask-scoring R-ConvNN (Region Convolutional Neural Network). This method works exceptionally well at accurately identifying text that is curved and text that has multiple orientations inside real-world input images. This study incorporates a mask-scoring R-ConvNN network framework to enhance the model's ability to score masks correctly for the observed occurrences. By providing more weight to accurate mask predictions, our scoring system eliminates inconsistencies between mask quality and score and enhances the effectiveness of instance segmentation. This paper also incorporates a Pyramid-based Text Proposal Network (PBTPN) and a Transformation Component Network (TCN) to enhance the feature extraction capabilities of the mask-scoring R-ConvNN for text identification and segmentation with the TSEA. Studies show that Pyramid Networks are especially effective in reducing false alarms caused by images with backgrounds that mimic text. On benchmark datasets ICDAR 2015, SCUT-CTW1500 containing multi-oriented and curved text, this method outperforms existing methods by conducting extensive testing across several scales and utilizing a single model. This study expands the field of vision-oriented applications by highlighting the growing significance of effectively locating and detecting text in natural situations

Text localization in natural images through effective re identification of the MSER

© 2017 Association for Computing Machinery. Text detection and recognition from images have numerous applications for document analysis and information retrieval tasks. An accurate and robust method for detecting texts in natural scene images is proposed in this paper. Text-region candidates are detected using maximally stable extremal regions (MSER) and a machine learning based method is then applied to refine and validate the initial detection. The effectiveness of features based on aspect ratio, GLSM, LBP, HOG descriptors are investigated. Text-region classifiers of MLP, SVM and RF are trained using selections of these features and their combination. A publicly available multilingual dataset ICDAR 2003,2011 has been used to evaluate the method. The proposed method achieved excellent performance on both databases and the improvements are significant in terms of Precision, Recall, and F-measure. The results show that using a suitable feature combination and selection approach can can significantly increase the accuracy of the algorithms

Text-detection and -recognition from natural images

Text detection and recognition from images could have numerous functional applications for document analysis, such as assistance for visually impaired people; recognition of vehicle license plates; evaluation of articles containing tables, street signs, maps, and diagrams; keyword-based image exploration; document retrieval; recognition of parts within industrial automation; content-based extraction; object recognition; address block location; and text-based video indexing. This research exploited the advantages of artificial intelligence (AI) to detect and recognise text from natural images. Machine learning and deep learning were used to accomplish this task.In this research, we conducted an in-depth literature review on the current detection and recognition methods used by researchers to identify the existing challenges, wherein the differences in text resulting from disparity in alignment, style, size, and orientation combined with low image contrast and a complex background make automatic text extraction a considerably challenging and problematic task. Therefore, the state-of-the-art suggested approaches obtain low detection rates (often less than 80%) and recognition rates (often less than 60%). This has led to the development of new approaches. The aim of the study was to develop a robust text detection and recognition method from natural images with high accuracy and recall, which would be used as the target of the experiments. This method could detect all the text in the scene images, despite certain specific features associated with the text pattern. Furthermore, we aimed to find a solution to the two main problems concerning arbitrarily shaped text (horizontal, multi-oriented, and curved text) detection and recognition in a low-resolution scene and with various scales and of different sizes.In this research, we propose a methodology to handle the problem of text detection by using novel combination and selection features to deal with the classification algorithms of the text/non-text regions. The text-region candidates were extracted from the grey-scale images by using the MSER technique. A machine learning-based method was then applied to refine and validate the initial detection. The effectiveness of the features based on the aspect ratio, GLCM, LBP, and HOG descriptors was investigated. The text-region classifiers of MLP, SVM, and RF were trained using selections of these features and their combinations. The publicly available datasets ICDAR 2003 and ICDAR 2011 were used to evaluate the proposed method. This method achieved the state-of-the-art performance by using machine learning methodologies on both databases, and the improvements were significant in terms of Precision, Recall, and F-measure. The F-measure for ICDAR 2003 and ICDAR 2011 was 81% and 84%, respectively. The results showed that the use of a suitable feature combination and selection approach could significantly increase the accuracy of the algorithms.A new dataset has been proposed to fill the gap of character-level annotation and the availability of text in different orientations and of curved text. The proposed dataset was created particularly for deep learning methods which require a massive completed and varying range of training data. The proposed dataset includes 2,100 images annotated at the character and word levels to obtain 38,500 samples of English characters and 12,500 words. Furthermore, an augmentation tool has been proposed to support the proposed dataset. The missing of object detection augmentation tool encroach to proposed tool which has the ability to update the position of bounding boxes after applying transformations on images. This technique helps to increase the number of samples in the dataset and reduce the time of annotations where no annotation is required. The final part of the thesis presents a novel approach for text spotting, which is a new framework for an end-to-end character detection and recognition system designed using an improved SSD convolutional neural network, wherein layers are added to the SSD networks and the aspect ratio of the characters is considered because it is different from that of the other objects. Compared with the other methods considered, the proposed method could detect and recognise characters by training the end-to-end model completely. The performance of the proposed method was better on the proposed dataset; it was 90.34. Furthermore, the F-measure of the method’s accuracy on ICDAR 2015, ICDAR 2013, and SVT was 84.5, 91.9, and 54.8, respectively. On ICDAR13, the method achieved the second-best accuracy. The proposed method could spot text in arbitrarily shaped (horizontal, oriented, and curved) scene text.</div