Data Mining Practical Machine Learning Tools and Techniques

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

It made it possible to improve people's lives. Now it prevents all forms of discrimination in the world. It helps to improve our world.

Universal Declaration of Human Rights

Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very useful and popular method in various applications. Accuracy of OCR can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. We begin this paper with an introduction of Optical Character Recognition (OCR) method, History of Open Source OCR tool Tesseract, architecture of it and experiment result of OCR performed by Tesseract on different kinds images are discussed. We conclude this paper by comparative study of this tool with other commercial OCR tool Transym OCR by considering vehicle number plate as input. From vehicle number plate we tried to extract vehicle number by using Tesseract and Transym and compared these tools based on various parameters. explained.Keywords like: Desktop OCR, Server OCR, Web OCR etc.

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Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study

The Trace transform proposed, a generalization of the Radon transform, consists of tracing an image with straight lines along which certain functionals of the image function are calculated. Different functionals that can be used may be invariant to different transformations of the image. The paper presents the properties the functionals must have in order to be useful in three different applications of the method: construction of invariant features to rotation, translation and scaling of the image, construction of sensitive features to the parameters of rotation, translation and scaling of the image, and construction of features that may correlate well with a certain phenomenon we wish to monitor.

The Trace transform and its applications.

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Skeletonization Algorithm for Binary Images

The binary image is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. In this regard, pattern recognition studies involve document analysis techniques. Optical font recognition is among the pattern recognition techniques that are becoming popular today. In this paper, we propose an enhanced global feature extraction method based on the on statistical analysis of the behavior of edge pixels in binary images. A novel method in feature extraction for binary images has been proposed whereby the behavior of the edge pixels between a white background and a black pattern in a binary image captures information about the properties of the pattern. The proposed method is tested on an Arabic calligraphic script image for an optical font recognition application. To evaluate the performance of our proposed method, we compared it with a gray-level co occurrence matrix (GLCM). We classified the features using a multilayer artificial immune system, a Bayesian network, decision table rules, a decision tree, and a multilayer network to identify which approach is most suitable for our proposed method. The results of the experiments show that the proposed method with a decision tree classifier can boost the overall performance of optical font recognition.

A novel statistical feature extraction method for textual images: Optical font recognition

The study of optical font recognition has becoming more popular nowadays. In line to that, global analysis approach is extensively used to identify various font type to classify writer identity. Objective of this paper is to propose an enhanced global analysis method. Based on statistical analysis of edge pixels relationships, a novel method in feature extraction for binary images has proposed. We test the proposed method on Arabic calligraphy script image for optical font recognition application. We classify those images using Multilayer Network, Bayes network and Decision Tree classifiers to identify the Arabic calligraphy type. The experiments results shows that our proposed method has boost up the overall performance of the optical font recognition.

A statistical global feature extraction method for optical font recognition

Document binarization is an 
important technique in document image analysis and recognition. Generally, binarization methods are ineffective for degraded images. Several binarization methods have been proposed; however, none of them are effective for historical and degraded document images. In this paper, a new binarization method is proposed for degraded document images. The proposed method based on the variance between pixel contrast, it consists of four stages: pre-processing, geometrical feature extraction, feature selection, and post-processing. The proposed method was evaluated based on several visual and statistical experiments. The experiments were conducted using five International Document Image Binarization Contest benchmark datasets specialized for binarization testing. The results compared with five adaptive binarization methods: Niblack, Sauvola thresholding, Sauvola compound algorithm, NICK, and Bataineh. The results show that the proposed method performs better than other methods in all binarization cases.

Bilal Bataineh

Papers

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Skeletonization Algorithm for Binary Images

A novel statistical feature extraction method for textual images: Optical font recognition

A statistical global feature extraction method for optical font recognition

Adaptive binarization method for degraded document images based on surface contrast variation