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

2019 ◽  
Vol 17 (3) ◽  
pp. 375-385
Author(s):  
Rashedul Islam ◽  
Rafiqul Islam ◽  
Kamrul Talukder
Keyword(s):  
Color Image ◽  
False Positives ◽  
Text Recognition ◽  
Filtering Method ◽  
Stroke Width ◽  
Benchmark Database ◽  
Maximally Stable Extremal Region ◽  
Stroke Width Transform ◽  
Detection And Localization ◽  
F Measure

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.

Multi-Lingual Scene Text Detection Using One-Class Classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 48-65 ◽  
Author(s):  
Anirban Mukhopadhyay ◽  
Sourav Kumar ◽  
Souvik Roy Chowdhury ◽  
Neelotpal Chakraborty ◽  
Ayatullah Faruk Mollah ◽  
...  
Keyword(s):  
Gabor Filter ◽  
False Positives ◽  
Text Detection ◽  
Detection Methods ◽  
Stroke Width ◽  
Scene Text Detection ◽  
Scene Text ◽  
Stroke Width Transform ◽  
One Class Classifier ◽  
Occurrence Matrix

The main purpose of scene text recognition is to detect texts in a given image. The problem of text detection and recognition in such images has gained great attention in recent years due to rising demand of several applications like visual based applications, multimedia and content-based retrieval. Due to low accuracies of existing scene text detection methods, an improved pipeline is developed for text localizing task. First, candidate text regions are generated using Maximally Stable Extremal Region and Stroke Width Transform methods that capture true positives along with many false positives. A One Class Classifier is trained to label the candidate regions obtained, as text or non-text, which in this case is suitable as non-text class cannot be adequately represented to train a binary classifier. The one class classifier is trained with some popular feature descriptors like Histogram of Oriented Gradients, Grey Level Co-Occurrence Matrix, Discrete Cosine Transform and Gabor filter. Experimental results show high recall for text containing regions and reducing false positives.

scholarly journals An enhanced binarization framework for degraded historical document images

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Xiong ◽  
Lei Zhou ◽  
Ling Yue ◽  
Lirong Li ◽  
Song Wang
Keyword(s):  
Document Image ◽  
Morphological Operations ◽  
Document Images ◽  
Minimum Entropy ◽  
Stroke Width ◽  
Background Estimation ◽  
Structuring Element ◽  
Document Image Binarization ◽  
Benchmark Datasets ◽  
Stroke Width Transform

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

scholarly journals Multipurpose Watermarking Based on SWT and SVD

2018 ◽  
Vol 7 (3.29) ◽  
pp. 65
Author(s):  
P Sivananthamaitrey ◽  
P Rajesh Kumar
Keyword(s):  
Copyright Protection ◽  
Color Image ◽  
High Capacity ◽  
Tamper Detection ◽  
Least Significant Bit ◽  
Green Component ◽  
Blue Component ◽  
Multipurpose Watermarking ◽  
Value Decomposition ◽  
Detection And Localization

Dual digital watermarking has emerged as a successful solution for copyright protection, tamper detection and localization. However, several problems related to the robustness, capacity, tampered area detection still mystifying. This paper presents a high capacity dual watermarking mechanism for digital color image. An invisible robust watermark is embedded in the Green component of the host image by using a hybrid combination of Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD) for copyright protection. A fragile invisible watermark based on the Least Significant Bit (LSB) replacement approach is embedded in the Blue component of the image for tamper detection and localization. The proposed technique focuses on robustness and imperceptibility while maximizing embedding capacity that makes this technique a multipurpose watermarking scheme.

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