2018 24th International Conference on Pattern Recognition (ICPR), 2018
Water image classification is challenging because water images of ocean or river share the same p... more Water image classification is challenging because water images of ocean or river share the same properties with images of polluted water such as fungus, waste and rubbish. In this paper, we present a method for classifying clean and polluted water images. The proposed method explores Fourier transform based features for extracting texture properties of clean and polluted water images. Fourier spectrum of each input image is divided into several sub-regions based on angle and spatial information. For each region over the spectrum, the proposed method extracts mean and variance features using intensity values, which results in a feature matrix. The feature matrix is then passed to an SVM classifier for the classification of clean and polluted water images. Experimental results on classes of clean and polluted water images show that the proposed method is effective. Furthermore, a comparative study with the state-of-the-art method shows that the proposed method outperforms the existing method in terms of classification rate, recall, precision and F-measure.
Scene text binarization and recognition is a challenging task due to different appearance of text... more Scene text binarization and recognition is a challenging task due to different appearance of text in clutter background and uneven illumination in natural scene images. In this paper, we present a new method based on adaptive histogram analysis for each sliding window over a word of a text line detected by the text detection method. The histogram analysis works on the basis that intensity values of text pixels in each sliding window have uniform color. The method segments the words based on region growing which studies spacing between words and characters. Then we propose to use existing OCRs such as ABBYY and Tesseract (Google) to recognize the text line at word and character levels to validate the binarization results. The method is compared with well-known global thresholding technique of binarization to show its effectiveness.
2018 24th International Conference on Pattern Recognition (ICPR), 2018
Text line segmentation from handwritten documents is challenging when a document image contains s... more Text line segmentation from handwritten documents is challenging when a document image contains severe touching. In this paper, we propose a new idea based on Weighted-Gradient Features (WGF) for segmenting text lines. The proposed method finds the number of zero crossing points for every row of Canny edge image of the input one, which is considered as the weights of respective rows. The weights are then multiplied with gradient values of respective rows of the image to widen the gap between pixels in the middle portion of text and the other portions. Next, k-means clustering is performed on WGF to classify middle and other pixels of text. The method performs morphological operation to obtain word components as patches for the result of clustering. The patches in both the clusters are matched to find common patch areas, which helps in reducing touching effect. Then the proposed method checks linearity and non-linearity iteratively based on patch direction to segment text lines. The method is tested on our own and standard datasets, namely, Alaei, ICDAR 2013 robust competition on handwriting context and ICDAR 2015-HTR, to evaluate the performance. Further, the method is compared with the state of art methods to show its effectiveness and usefulness.
There are situations where it is not possible to capture a large document with a given imaging me... more There are situations where it is not possible to capture a large document with a given imaging media such as scanner or copying machine in a single stretch because of their inherent limitations. This results in capturing a large document in terms of split components of a ...
Skew angle estimation is an important component of optical character recognition (OCR) systems an... more Skew angle estimation is an important component of optical character recognition (OCR) systems and document analysis systems (DAS). In this paper, a novel and an efficient method to estimate the skew angle of a scanned document image is proposed. The proposed method has ...
2011 International Conference on Document Analysis and Recognition, 2011
Abstract This paper presents a new method based on Fourier and moments features to extract words ... more Abstract This paper presents a new method based on Fourier and moments features to extract words and characters from a video text line in any direction for recognition. Unlike existing methods which output the entire text line to the ensuing recognition algorithm, the ...
2018 24th International Conference on Pattern Recognition (ICPR), 2018
Water image classification is challenging because water images of ocean or river share the same p... more Water image classification is challenging because water images of ocean or river share the same properties with images of polluted water such as fungus, waste and rubbish. In this paper, we present a method for classifying clean and polluted water images. The proposed method explores Fourier transform based features for extracting texture properties of clean and polluted water images. Fourier spectrum of each input image is divided into several sub-regions based on angle and spatial information. For each region over the spectrum, the proposed method extracts mean and variance features using intensity values, which results in a feature matrix. The feature matrix is then passed to an SVM classifier for the classification of clean and polluted water images. Experimental results on classes of clean and polluted water images show that the proposed method is effective. Furthermore, a comparative study with the state-of-the-art method shows that the proposed method outperforms the existing method in terms of classification rate, recall, precision and F-measure.
Scene text binarization and recognition is a challenging task due to different appearance of text... more Scene text binarization and recognition is a challenging task due to different appearance of text in clutter background and uneven illumination in natural scene images. In this paper, we present a new method based on adaptive histogram analysis for each sliding window over a word of a text line detected by the text detection method. The histogram analysis works on the basis that intensity values of text pixels in each sliding window have uniform color. The method segments the words based on region growing which studies spacing between words and characters. Then we propose to use existing OCRs such as ABBYY and Tesseract (Google) to recognize the text line at word and character levels to validate the binarization results. The method is compared with well-known global thresholding technique of binarization to show its effectiveness.
2018 24th International Conference on Pattern Recognition (ICPR), 2018
Text line segmentation from handwritten documents is challenging when a document image contains s... more Text line segmentation from handwritten documents is challenging when a document image contains severe touching. In this paper, we propose a new idea based on Weighted-Gradient Features (WGF) for segmenting text lines. The proposed method finds the number of zero crossing points for every row of Canny edge image of the input one, which is considered as the weights of respective rows. The weights are then multiplied with gradient values of respective rows of the image to widen the gap between pixels in the middle portion of text and the other portions. Next, k-means clustering is performed on WGF to classify middle and other pixels of text. The method performs morphological operation to obtain word components as patches for the result of clustering. The patches in both the clusters are matched to find common patch areas, which helps in reducing touching effect. Then the proposed method checks linearity and non-linearity iteratively based on patch direction to segment text lines. The method is tested on our own and standard datasets, namely, Alaei, ICDAR 2013 robust competition on handwriting context and ICDAR 2015-HTR, to evaluate the performance. Further, the method is compared with the state of art methods to show its effectiveness and usefulness.
There are situations where it is not possible to capture a large document with a given imaging me... more There are situations where it is not possible to capture a large document with a given imaging media such as scanner or copying machine in a single stretch because of their inherent limitations. This results in capturing a large document in terms of split components of a ...
Skew angle estimation is an important component of optical character recognition (OCR) systems an... more Skew angle estimation is an important component of optical character recognition (OCR) systems and document analysis systems (DAS). In this paper, a novel and an efficient method to estimate the skew angle of a scanned document image is proposed. The proposed method has ...
2011 International Conference on Document Analysis and Recognition, 2011
Abstract This paper presents a new method based on Fourier and moments features to extract words ... more Abstract This paper presents a new method based on Fourier and moments features to extract words and characters from a video text line in any direction for recognition. Unlike existing methods which output the entire text line to the ensuing recognition algorithm, the ...
Uploads
Papers by P Shivakumara