Document binarization is an active research area for many years. The choice of the most appropria... more Document binarization is an active research area for many years. The choice of the most appropriate binarization algorithm for each case proved to be a very difficult procedure itself. In this paper, we propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesn't require anything more than the binarization stage. As a demonstration of the proposed technique, we use the case of degraded historical documents. Then we apply the proposed technique to 30 binarization algorithms. Experimental results and conclusions are presented.
This paper proposes a new method for reduction of the number of colors in an image. The proposed ... more This paper proposes a new method for reduction of the number of colors in an image. The proposed approach uses both the image color components and local image characteristics to feed a Kohonen Self-Organized Feature Map (SOFM) neural network. After training, the neurons of the output competition layer define the proper color classes. The final image has not only the dominant image colors, but also its texture approaches the image local characteristics used. To speedup the entire algorithm and reduce memory requirements, a fractal scanning sub-sampling technique can be used. The method is applicable to any type of color images and can be easily extended to accommodate any type of spatial characteristics. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.
Proceedings of 13th International Conference on Digital Signal Processing, 1997
Abstract This paper describes a new method that clusters the content of a mixed type document in ... more Abstract This paper describes a new method that clusters the content of a mixed type document in text or nontext areas. The proposed approach is based on a new set of textural features combined with a two stage neural network classifier. The neural network classifier ...
21st Mediterranean Conference on Control and Automation, 2013
ABSTRACT In this paper, a novel method to convert color documents to grayscale is proposed. This ... more ABSTRACT In this paper, a novel method to convert color documents to grayscale is proposed. This approach takes as criteria that a suitable form of a grayscale document must have locally uniform background, well separated characters from the background and reduced noise. The main stages of the proposed technique are color reduction to a limited number of dominant colors and transformation of the gray classes obtained 3-D to a more compact form. The resultant grayscale document gives better OCR results and better compression ratio.
A new method for text localization in cover color pages and general color document images is pres... more A new method for text localization in cover color pages and general color document images is presented. The colors of the document image are reduced to a small number using a color reduction technique based on a Kohonen Self Organized Map (KSOM) neural network. Each color defines a color plane in which the connected components (CCs) are extracted. In each color plane a CC filtering procedure is applied which is followed by a local grouping procedure. At the end of this stage, groups of CCs are constructed which are next refined by obtaining the Direction Of Connection (DOC) property for each CC. Using the DOC property, the groups of CCs are classified as text or non text regions. Finally, text regions identified in the different color planes are superimposed and the final text localization of the entire document is achieved. The proposed technique was extensively tested with a large number of color documents.
A new method for the reduction of the number of colors in a digital image is proposed. The new me... more A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the developed of a new neural network classifier that combines the advantages of the Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (SOFM) neural networks. We call the new neural network: Self-Growing and SelfOrganized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. As a consecutive, isolated color classes, which may correspond to significant image details, can be obtained. The SGONG is fed by the color components and additional spatial features. To speed up the entire algorithm and to reduce memory requirements, a fractal scanning sub-sampling technique is used. The method is applicable to any type of color images and it can accommodate any type of color space.
Series in Machine Perception and Artificial Intelligence, 1994
This paper describes a new method for character recognition of typewritten text. The proposed app... more This paper describes a new method for character recognition of typewritten text. The proposed approach is based on the approximation of character signatures by rational functions. Specifically, after the preprocessing operation, a separation procedure is applied to each character and its one-dimensional signatures are derived. These signatures are then approximated by rational functions via a linear programming technique and according to the minimax criterion. The values of the approximation errors for each signature are specified as character features. Through this technique only six powerful features are derived for each character. The classification technique employed is simple, adapted to the features selected and is based on features’ similarities in combination with the minimum Euclidean distance classifier.
International Journal of Pattern Recognition and Artificial Intelligence, 2008
In this paper we propose a technique for detecting and correcting the skew of text areas in a doc... more In this paper we propose a technique for detecting and correcting the skew of text areas in a document. The documents we work with may contain several areas of text with different skew angles. First, a text localization procedure is applied based on connected components analysis. Specifically, the connected components of the document are extracted and filtered according to their size and geometric characteristics. Next, the candidate characters are grouped using a nearest neighbor approach to form words and then based on these words text lines of any skew are constructed. Then, the top-line and baseline for each text line are estimated using linear regression. Text lines in near locations, having similar skew angles, are grown to form text areas. For each text area a local skew angle is estimated and then these text areas are skew corrected independently to horizontal or vertical orientation. The technique has been extensively tested on a variety of document images and its accuracy ...
Document binarization is an active research area for many years. The choice of the most appropria... more Document binarization is an active research area for many years. The choice of the most appropriate binarization algorithm for each case proved to be a very difficult procedure itself. In this paper, we propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesn't require anything more than the binarization stage. As a demonstration of the proposed technique, we use the case of degraded historical documents. Then we apply the proposed technique to 30 binarization algorithms. Experimental results and conclusions are presented.
This paper proposes a new method for reduction of the number of colors in an image. The proposed ... more This paper proposes a new method for reduction of the number of colors in an image. The proposed approach uses both the image color components and local image characteristics to feed a Kohonen Self-Organized Feature Map (SOFM) neural network. After training, the neurons of the output competition layer define the proper color classes. The final image has not only the dominant image colors, but also its texture approaches the image local characteristics used. To speedup the entire algorithm and reduce memory requirements, a fractal scanning sub-sampling technique can be used. The method is applicable to any type of color images and can be easily extended to accommodate any type of spatial characteristics. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.
Proceedings of 13th International Conference on Digital Signal Processing, 1997
Abstract This paper describes a new method that clusters the content of a mixed type document in ... more Abstract This paper describes a new method that clusters the content of a mixed type document in text or nontext areas. The proposed approach is based on a new set of textural features combined with a two stage neural network classifier. The neural network classifier ...
21st Mediterranean Conference on Control and Automation, 2013
ABSTRACT In this paper, a novel method to convert color documents to grayscale is proposed. This ... more ABSTRACT In this paper, a novel method to convert color documents to grayscale is proposed. This approach takes as criteria that a suitable form of a grayscale document must have locally uniform background, well separated characters from the background and reduced noise. The main stages of the proposed technique are color reduction to a limited number of dominant colors and transformation of the gray classes obtained 3-D to a more compact form. The resultant grayscale document gives better OCR results and better compression ratio.
A new method for text localization in cover color pages and general color document images is pres... more A new method for text localization in cover color pages and general color document images is presented. The colors of the document image are reduced to a small number using a color reduction technique based on a Kohonen Self Organized Map (KSOM) neural network. Each color defines a color plane in which the connected components (CCs) are extracted. In each color plane a CC filtering procedure is applied which is followed by a local grouping procedure. At the end of this stage, groups of CCs are constructed which are next refined by obtaining the Direction Of Connection (DOC) property for each CC. Using the DOC property, the groups of CCs are classified as text or non text regions. Finally, text regions identified in the different color planes are superimposed and the final text localization of the entire document is achieved. The proposed technique was extensively tested with a large number of color documents.
A new method for the reduction of the number of colors in a digital image is proposed. The new me... more A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the developed of a new neural network classifier that combines the advantages of the Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (SOFM) neural networks. We call the new neural network: Self-Growing and SelfOrganized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. As a consecutive, isolated color classes, which may correspond to significant image details, can be obtained. The SGONG is fed by the color components and additional spatial features. To speed up the entire algorithm and to reduce memory requirements, a fractal scanning sub-sampling technique is used. The method is applicable to any type of color images and it can accommodate any type of color space.
Series in Machine Perception and Artificial Intelligence, 1994
This paper describes a new method for character recognition of typewritten text. The proposed app... more This paper describes a new method for character recognition of typewritten text. The proposed approach is based on the approximation of character signatures by rational functions. Specifically, after the preprocessing operation, a separation procedure is applied to each character and its one-dimensional signatures are derived. These signatures are then approximated by rational functions via a linear programming technique and according to the minimax criterion. The values of the approximation errors for each signature are specified as character features. Through this technique only six powerful features are derived for each character. The classification technique employed is simple, adapted to the features selected and is based on features’ similarities in combination with the minimum Euclidean distance classifier.
International Journal of Pattern Recognition and Artificial Intelligence, 2008
In this paper we propose a technique for detecting and correcting the skew of text areas in a doc... more In this paper we propose a technique for detecting and correcting the skew of text areas in a document. The documents we work with may contain several areas of text with different skew angles. First, a text localization procedure is applied based on connected components analysis. Specifically, the connected components of the document are extracted and filtered according to their size and geometric characteristics. Next, the candidate characters are grouped using a nearest neighbor approach to form words and then based on these words text lines of any skew are constructed. Then, the top-line and baseline for each text line are estimated using linear regression. Text lines in near locations, having similar skew angles, are grown to form text areas. For each text area a local skew angle is estimated and then these text areas are skew corrected independently to horizontal or vertical orientation. The technique has been extensively tested on a variety of document images and its accuracy ...
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