Detecting text in multi-colour images is an important prerequisite. The RBG image is converted into YUV image, after that the multidimensional filter is used to reduce the noise in the YUV image. Canny edge detection is used to measure the continuity of the edges in the images. A efficient text detection is proposed using stroke width transformation method based on contours which can effectively remove the interference of non-stroke edges in complex background and the importance of recent feature (inter-frame feature), in the part of caption extraction(detection, localization). The horizontal and vertical histogram basis is used to calculate the luminance and chrominance which defines the background. Moreover the morphological operation which removes non text areas in the boundaries. Since some background pixels can also have the similar colour, some false stroke areas or character pixels are possible to appear in the output image, which will degrade the recognition rate of OCR (optical character recognition). It exploits the temporal homogeneity of colour of text pixels to filter out some background pixels with similar colour. Optical character recognition enables us to successfully extract the text from an image and convert it into an editable text document. Experimental results evaluated on the Neural network classifier which performance training and testing methods. Training dataset show that our accession yields higher precision and performance compared with forefront methods. The experimental results demonstrate the proposed method will provides efficient result than the existing technique.
| Published in | Machine Learning Research (Volume 1, Issue 1) |
| DOI | 10.11648/j.mlr.20160101.13 |
| Page(s) | 19-32 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Image Segmentation, Stroke Width Transformation (SWT), Connected Component Analysis (CCA), Histogram of Gradients (HOG), Edge Detection, Neural Network Classifier, Optical Character Recognition
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APA Style
S. Kannadhasan, R. Rajesh Baba. (2017). A Novel Approach to Detect Text in Various Dynamic-Colour Images. Machine Learning Research, 1(1), 19-32. https://doi.org/10.11648/j.mlr.20160101.13
ACS Style
S. Kannadhasan; R. Rajesh Baba. A Novel Approach to Detect Text in Various Dynamic-Colour Images. Mach. Learn. Res. 2017, 1(1), 19-32. doi: 10.11648/j.mlr.20160101.13
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Download@article{10.11648/j.mlr.20160101.13,
author = {S. Kannadhasan and R. Rajesh Baba},
title = {A Novel Approach to Detect Text in Various Dynamic-Colour Images},
journal = {Machine Learning Research},
volume = {1},
number = {1},
pages = {19-32},
doi = {10.11648/j.mlr.20160101.13},
url = {https://doi.org/10.11648/j.mlr.20160101.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20160101.13},
abstract = {Detecting text in multi-colour images is an important prerequisite. The RBG image is converted into YUV image, after that the multidimensional filter is used to reduce the noise in the YUV image. Canny edge detection is used to measure the continuity of the edges in the images. A efficient text detection is proposed using stroke width transformation method based on contours which can effectively remove the interference of non-stroke edges in complex background and the importance of recent feature (inter-frame feature), in the part of caption extraction(detection, localization). The horizontal and vertical histogram basis is used to calculate the luminance and chrominance which defines the background. Moreover the morphological operation which removes non text areas in the boundaries. Since some background pixels can also have the similar colour, some false stroke areas or character pixels are possible to appear in the output image, which will degrade the recognition rate of OCR (optical character recognition). It exploits the temporal homogeneity of colour of text pixels to filter out some background pixels with similar colour. Optical character recognition enables us to successfully extract the text from an image and convert it into an editable text document. Experimental results evaluated on the Neural network classifier which performance training and testing methods. Training dataset show that our accession yields higher precision and performance compared with forefront methods. The experimental results demonstrate the proposed method will provides efficient result than the existing technique.},
year = {2017}
}
TY - JOUR T1 - A Novel Approach to Detect Text in Various Dynamic-Colour Images AU - S. Kannadhasan AU - R. Rajesh Baba Y1 - 2017/01/19 PY - 2017 N1 - https://doi.org/10.11648/j.mlr.20160101.13 DO - 10.11648/j.mlr.20160101.13 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 19 EP - 32 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20160101.13 AB - Detecting text in multi-colour images is an important prerequisite. The RBG image is converted into YUV image, after that the multidimensional filter is used to reduce the noise in the YUV image. Canny edge detection is used to measure the continuity of the edges in the images. A efficient text detection is proposed using stroke width transformation method based on contours which can effectively remove the interference of non-stroke edges in complex background and the importance of recent feature (inter-frame feature), in the part of caption extraction(detection, localization). The horizontal and vertical histogram basis is used to calculate the luminance and chrominance which defines the background. Moreover the morphological operation which removes non text areas in the boundaries. Since some background pixels can also have the similar colour, some false stroke areas or character pixels are possible to appear in the output image, which will degrade the recognition rate of OCR (optical character recognition). It exploits the temporal homogeneity of colour of text pixels to filter out some background pixels with similar colour. Optical character recognition enables us to successfully extract the text from an image and convert it into an editable text document. Experimental results evaluated on the Neural network classifier which performance training and testing methods. Training dataset show that our accession yields higher precision and performance compared with forefront methods. The experimental results demonstrate the proposed method will provides efficient result than the existing technique. VL - 1 IS - 1 ER -
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