Antonios Atsalakis

Text extraction in complex color documents

Text extraction in mixed-type documents is a pre-processing and necessary stage for many document applications. In mixed-type color documents, text, drawings and graphics appear with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. In this paper, a new method to automatically detect and extract text in mixed-type color documents is presented. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors and to convert the document into the principal of them. Then, using the principal colors, the document image is split into the separable color plains. Thus, binary images are obtained, each one corresponding to a principal color. The PLA technique is applied independently to each of the color plains and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color plains and to produce the final document. Several experimental and comparative results, exhibiting the performance of the proposed technique, are also presented.

Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas

A new method for color reduction in a digital image is proposed, which is based on the development of a new neural network classifier and on a new method for Estimation of the Most Important Classes (EMIC). The proposed neural network combines the features of the well-known Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (KSOFM) neural networks. We call the new neural network Self-Growing and Self-Organized Neural Gas (SGONG). This combination produces a new neural network with outstanding features. The proposed technique utilizes the GNG mechanism of growing the neural lattice and the KSOFM leaning adaptation mechanism. Besides, introducing a number of criteria that have an effect on inserting or removing neurons, it is able to automatically define the number of the created neurons and their topology. Moreover, applying the EMIC method, the produced classes can be filtered and the most important classes can be found. The combination of SGONG and EMIC results in retaining the isolated and significant colors with the minimum number of color classes. The above techniques are able to be fed by both color and spatial features. For this reason a similarity function is used for vector comparison. The method is applicable to any type of color images and it can accommodate any type of color space.

Gray-Level Reduction Using Local Spatial Features

This paper proposes a new method for reduction of the number of gray-levels in an image. The proposed approach achieves gray-level reduction using both the image gray-levels and additional local spatial features. Both gray-level and local feature values feed a self-organized neural network classifier. After training, the neurons of the output competition layer of the SOFM define the gray-level classes. The final image has not only the dominant image gray-levels, but also has a texture approaching the image local characteristics used. To split the initial classes further, the proposed technique can be used in an adaptive mode. To speed up the entire multithresholding algorithm and reduce memory requirements, a fractal scanning subsampling technique is adopted. The method is applicable to any type of gray-level image and can be easily modified to accommodate any type of spatial characteristic. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.

On Estimation of the Number of Image Principal Colors and Color Reduction through Self-Organized Neural Networks

A new technique suitable for reduction of the number of colors in a color image is presented in this article. It is based on the use of the image Principal Color Components (PCC), which consist of the image color components and additional image components extracted with the use of proper spatial features. The additional spatial features are used to enhance the quality of the final image. First, the principal colors of the image and the principal colors of each PCC are extracted. Three algorithms were developed and tested for this purpose. Using Kohonen self‐organizing feature maps (SOFM) as classifiers, the principal color components of each PCC are obtained and a look‐up table, containing the principal colors of the PCC, is constructed. The final colors are extracted from the look‐up table entries through a SOFM by setting the number of output neurons equal to the number of the principal colors obtained for the original image. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is employed. The method is independent of the color scheme; it is applicable to any type of color images and can be easily modified to accommodate any type of spatial features. Several experimental and comparative results exhibiting the performance of the proposed technique are presented.

Hand gesture recognition via a new self-organized neural network

A new method for hand gesture recognition is proposed which is based on an innovative Self-Growing and Self-Organized Neural Gas (SGONG) network. Initially, the region of the hand is detected by using a color segmentation technique that depends on a skin-color distribution map. Then, the SGONG network is applied on the segmented hand so as to approach its topology. Based on the output grid of neurons, palm geometric characteristics are obtained which in accordance with powerful finger features allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a probability-based classification method.

Histogram Based Color Reduction through Self-Organized Neural Networks

A new technique suitable for reduction of the number of colors in an image is presented in this paper. It is based on histogram processing and the use of Kohonen Self Organizing Feature Map (SOFM) neural networks. Initially, the dominant colors of each primary image are extracted through a simple linear piece-wise histogram approximation process. Then, using a SOFM the dominant color components of each primary color band are obtained and a look up table is constructed containing all possible color triplets. The final dominant colors are extracted from the look-up table entries using a SOFM by specifying the number of output neurons equal to the number of the dominant colors. Thus, the final image has all the dominant color classes. Experimental and comparative results demonstrate the applicability of the proposed technique.

A window-based color quantization technique and its embedded implementation

A new color quantization (CQ) technique and its VLSI implementation is introduced. It is based on image split into windows and uses Kohonen self organized neural network classifier (SONNC). Initially, the dominant colors of each window are extracted through the SONNC and then are used for the quantization of the colors of the entire image. The image split in windows offers reduction of the memory requirements and feasibility of suitable VLSI implementation of the most time consuming part of the technique. Applying a systematic design methodology into the developed CQ algorithm, an efficient system-on-chip based on the ARM processor, which achieves high speed processing and less energy consumption, is derived.

Locating text in color documents

In complex color documents, text, drawings and graphics occur with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. A new method is proposed for the automatic detection and extraction of text in mixed type color documents. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors. Then, the image is split to separable binary images, each one corresponding to every principal color. The PLA technique is applied independently to each one of the color planes and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color planes and to produce the final document.

Image dominant colors estimation and color reduction via a new self-growing and self-organized neural gas

A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the development 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 Self-Organized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. Besides, a new method is proposed for the Estimation of the Most Important of already created Classes (EMIC). The combination of SGONG and EMIC in color images results in retaining the isolated and significant colors with the minimum number of color classes. The above techniques are able to be fed by both color and spatial features. For this reason a similarity function is used for vector comparison. 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.

Text identification in color documents

In complex color documents, text, drawings and graphics are appeared with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. In this paper, a new method is proposed to automatically detect and extract text in mixed type color documents. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors. Then, image is split to separable binary images, each one corresponding to every principal color. The PLA technique is applied independently to each one of the color plains and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color plains and to produce the final document.