Localized Text Regions Research Articles

ADNet: Rethinking the Shrunk Polygon-Based Approach in Scene Text Detection

To localize text regions and separate close instances, the shrunk polygon is widely used in recent scene text detection methods. However, there exist two problems: 1) Existing methods fail to consider the aspect ratio sensitive problem when reconstructing the text instance from shrunk polygon. 2) Texts with extreme aspect ratios will lead to the fracture of shrunk polygons. To handle these two problems, in this paper, we propose a novel Adaptive Dilation Network (ADNet) to focus on the reconstruction process from shrunk polygon, which aims to provide a tight and complete text representation. Firstly, instead of using a fixed dilation factor, ADNet uses an aspect ratio-wise dilation factor to reconstruct the text region from shrunk polygon for each text instance. Such an instance-wise dilation factor considers the scale correlation between the original and shrunk polygon, and thus can guide an adaptive text region reconstruction for texts with large aspect ratio variance. Secondly, to deal with the fracture of detection results, a new Efficient Spatial Relationship Module (ESRM) is devised to capture long-range dependencies with low computation cost. ESRM uses a novel Weighted Pooling to reduce the resolution of feature maps without much information loss. Compared with the existing methods, ADNet further explores the potential of shrunk polygon-based approaches and obtains excellent detection results at an impressive speed. Extensive experiments on several datasets (Total-Text, CTW1500, MSRA-TD500 and ICDAR2015) verify the superiority of our method. Code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qqqyd/ADNet</uri> .

Dominating set based arbitrary oriented bilingual scene text localization

&lt;p&gt;Localizing and recognizing arbitrarily oriented text in natural scene images is the biggest challenge. It is because scene texts are often erratic in shapes. This paper presents a simple and effective graph representational algorithm for detecting arbitrary-oriented text location to smoothen the text recognition process because of its high impact and simplicity of representation. An arbitrarily oriented text can be horizontal, vertical, perspective, curved (diagonal/off-diagonal), or even a combination. As a pre-processing step, image enhancement is performed in the frequency domain to improve the representation of images that are invariant to intensity. It is necessary to draw bounding boxes for each candidate character in the scene images to extract text regions. This step is carried out by utilizing the advantage of the region-based approach called maximally stable extremal regions. A typical problem with curved text localization is that non-text objects may occur within localized text regions. Our method is the first in the literature that searches for dominating sets to solve this problem. This dominating set method outperforms several traditional methods, including deep learning methods used for arbitrary text localization, on challenging datasets like 13&lt;sup&gt;th&lt;/sup&gt; international conference on document analysis and recognition (ICDAR 2015), multi-script robust reading competition (MRRC), CurvedText 80 (CUTE80), and arbitrary text (ArT).&lt;/p&gt;

The Smart Assistant for Library Management and Book Reader for Blind People Using Raspberry Pi

This system proposes a camera-based assistive text reading framework to help blind persons ad text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we rest propose an ancient and active motion-based method to dine a Region Of Interest (ROI) in the video by asking the user to shake the object. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Ad boost model. Text characters in the localized text regions are then binaries and recognized by the shelf Optical Character Recognition (OCR) software.

FREE: A Fast and Robust End-to-End Video Text Spotter.

Currently, video text spotting tasks usually fall into the four-staged pipeline: detecting text regions in individual images, recognizing localized text regions frame-wisely, tracking text streams and post-processing to generate final results. However, they may suffer from the huge computational cost as well as sub-optimal results due to the interferences of low-quality text and the none-trainable pipeline strategy. In this article, we propose a fast and robust end-to-end video text spotting framework named FREE by only recognizing the localized text stream one-time instead of frame-wise recognition. Specifically, FREE first employs a well-designed spatial-temporal detector that learns text locations among video frames. Then a novel text recommender is developed to select the highest-quality text from text streams for recognizing. Here, the recommender is implemented by assembling text tracking, quality scoring and recognition into a trainable module. It not only avoids the interferences from the low-quality text but also dramatically speeds up the video text spotting. FREE unites the detector and recommender into a whole framework, and helps achieve global optimization. Besides, we collect a large scale video text dataset for promoting the video text spotting community, containing 100 videos from 21 real-life scenarios. Extensive experiments on public benchmarks show our method greatly speeds up the text spotting process, and also achieves the remarkable state-of-the-art.

An Enhanced MSER Pruning Algorithm for Detection and Localization of Bangla Texts from Scene Images

Text detection and localization have great importance for content based image analysis and text based image indexing. The efficiency of text recognition depends on the efficiency of text localization. So, the main goal of the proposed method is to detect and localize text regions with high accuracy. To achieve this goal, a new and efficient method has been introduced for localization of Bangla text from scene images. In order to improve precision and recall as well as f-measure, Maximally Stable Extremal Region (MSER) based method along with double filtering techniques have been used. As MSER algorithm generates many false positives, we have introduced double filtering method for removing these false positives to increase the f-measure to a great extent. Our proposed method works at three basic levels. Firstly, MSER regions are generated from the input color image by converting it into gray scale image. Secondly, some heuristic features are used to filter out most of the false positives or non-text regions. Lastly, Stroke Width Transform (SWT) based filtering method is used to filter out remaining non-text regions. Remaining components are then grouped into candidate text regions marked by bounding box over each region. As there is no benchmark database for Bangla text, the proposed method is implemented on our own prepared database consisting of 200 scene images of Bangla texts and has got prominent performance. To evaluate the performance of our proposed approach, we have also tested the proposed method on International Conference on Document Analysis and Recognition( ICDAR) 2013 benchmark database and have got a better result than the related existing methods.

Text localization in camera captured images using fuzzy distance transform based adaptive stroke filter

Localization of text from camera captured images with complex background is now-a-days a growing demand of modern IT enable service. Most of the current text localization techniques are sensitive to text features like color, size, style and also to the background clutter. Among all the methods proposed in different literatures, Stroke Filter is much more effective in localization of text. The effectiveness of traditional stroke filter is limited because of its fixed width and is capable of segmenting strokes/texts of predefined range of width. The proposed method uses Fuzzy Distance Transform based adaptive stroke filter which can effectively localize text regions from camera captured images with complex background. The method is applied by experiment on a database containing 600 images and the visual response of text segmentation is quite impressive. To get the accuracy of the proposed method, it is applied on a set of 16 test images and the segmentation result is compared with the ground truth images resulting in a recall, precision and f-measure values of 96.65%, 87.77% and 91.89% respectively.

PORTABLE CAMERA-BASED ASSISTIVE TEXT AND PRODUCT LABEL READING FROM HAND-HELD OBJECTS FOR BLIND PERSONS

We propose a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we first propose an efficient and effective motionbased method to define a region of interest (ROI) in the video by asking the user to shake the object. This method extracts moving object region by a mixture of- Gaussians based background subtraction method. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels. Text characters in the localized text regions are recognized by off-the-shelf optical character recognition (OCR) software. The recognized text codes are output to blind users in speech. The proof-of-concept prototype is also evaluated on a dataset collected using 10 blind persons, to evaluate the effectiveness of the system's hardware. We explore user interface issues, and assess robustness of the algorithm in extracting and reading text nfrom different objects with complex backgrounds.

Text Localization and Character Extraction in Natural Scene Images using Contourlet Transform and SVM Classifier

The objective of this study is to propose a new method for text region localization and character extraction in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts multilingual text from natural scene image with cluttered backgrounds. The proposed approach involves four steps. First, potential text regions in an image are extracted based on edge features using Contourlet transform. In the second step, potential text regions are tested for text content or non-text using GLCM features and SVM classifier. In the third step, detection of multiple lines in localized text regions is done and line segmentation is performed using horizontal profiles. In the last step, each character of the segmented line is extracted using vertical profiles. The experimentation has been done using images drawn from own dataset and ICDAR dataset. The performance is measured in terms of the precision and recall. The results demonstrate the effectiveness of the proposed method, which can be used as an efficient method for text recognition in natural scene images.

Fast and accurate scene text understanding with image binarization and off-the-shelf OCR

While modern off-the-shelf OCR engines show particularly high accuracy on scanned text, text detection and recognition in natural images still remain a challenging problem. Here, we demonstrate that OCR engines can still perform well on this harder task as long as an appropriate image binarization is applied to input photographs. We propose a new binarization algorithm that is particularly suitable for scene text and systematically evaluate its performance along with 12 existing binarization methods. While most existing binarization techniques are designed specifically either for text detection or for recognition of localized text, our method shows very similar results for both large images and localized text regions. Therefore, it can be applied to large images directly with no need for re-binarization of localized text regions. We also propose the real-time variant of this method based on linear-time bilateral filtering. Evaluation across different metrics on established natural image text recognition benchmarks (ICDAR 2003 and ICDAR 2011) shows that our simple and fast image binarization method combined with off-the-shelf OCR engine achieves state-of-the-art performance for end-to-end text understanding in natural images and outperforms recent fancy methods.

Portable Camera-Based Assistive Text and Product Label Reading From Hand-Held Objects for Blind Persons

We propose a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we first propose an efficient and effective motion-based method to define a region of interest (ROI) in the video by asking the user to shake the object. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binarized and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm is quantitatively evaluated on ICDAR-2003 and ICDAR-2011 Robust Reading Datasets. Experimental results demonstrate that our algorithm achieves the state of the arts. The proof-of-concept prototype is also evaluated on a dataset collected using ten blind persons to evaluate the effectiveness of the system's hardware. We explore user interface issues and assess robustness of the algorithm in extracting and reading text from different objects with complex backgrounds.

Text Detection and Localization in Low Quality Video Images through Image Resolution Enhancement Technique

text information plays an important role in semantic- based video analysis, indexing, and retrieval. Text embedded in the image or video contains very useful information like the name of the person, title, location and sometimes brief description of the image. Many algorithms have been proposed to detect and localize the text information present in video images. In this paper, we proposed a methodology to enhance the quality of the image and then detect and localize text regions from low quality video images. Experimental results show the proposed method achieves improved precision rate and recall rate. them to localize text regions. Edge based methods focus on the high contrast between the background and text and the edges of the text boundary are identified and merged. Later several heuristics are required to filter out non-text regions. But, the presence of noise, complex background, and significant degradation in the low resolution image can affect the extraction of connected components and identification of boundary lines, thus making both the approaches inefficient. Texture analysis techniques are good choice for solving such a problem as they give global measure of properties of a region. In this paper, resolution enhancement technique has been employed on low quality video images by using interpolation which generates sharper high resolution image. Texture based text detection and segmentation is applied on the enhanced image. The proposed method is robust enough to detect text regions from low quality video images, and achieves improved precision rate and recall rate.