Document Images Are Acquired By Scanning Journal

Аvаilаbility: This system will retrieve the hаndwritten text regions only if the imаge contаins written text in it.

Next a black and white transition map is computed by counting the black and white transitions of every pixel using a horizontal window centered at the pixel in question. The horizontal window has a size of 3h where h Is the median height of a text line. Like the ALCM method, the transition map is then binarized in order to expose areas of the document that are likely to be text lines. A cut/max-flow graph cut algorithm is used to split up text areas that appear to encompass more than one line of text.

Ingest the letter data and analyze it. Text analysis and OCR, probably using data science tools to create a sanitized and structured data view tied to the GIS and address

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

The main focus of this project is reducing the feature extraction time of the system. As a conclusion, it shows that our framework extracts the features from the parse tree very fast. This paper can be further enhanced by using the hybrid classification algorithm to get more accuracy in classification. In this paper, the parse tree is obtained from the PostgreSQL databases and in future, it will get from MySQL databases. To decrease the feature extraction time, fragmented files will be processed in

Thus our proposed optimal feature subset selection based on multi-level feature subset selection produced better results based on number of subset feature produced and classifier performance. The future scope of the work is to use these features to annotate the image regions, so that the image retrieval system can retrieve relevant images based on image semantics.

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

The basic principle of this algorithm is to recognize the input paper currency. First of all acquired the image from a particular source. As in this thesis we use for reference images. System read the particular image. Then resize the image. After that the color separator convert the image into RGB to Gray scale and then in binary image. After that the system use color noise median filter. The currency length detector detects the length of the currency. Using the feature extraction techniques the system detect the particular feature of that currency and then the system use pattern matching algorithm to math that particular feature. The input image match with particular database image and according to that we find the currency. In this way this thesis design a automatic system in which we can recognized the paper currency.

Automatic Number Plate Recognition (ANPR) was firstly introduced in 1976 and gained much popularity during the last decade along with the improvement of digital cameras. ANPR is an image processing technology which allows extracting vehicle license plate number from digital camera images. It consists of a camera

The number plates are varied from one and another such as different material use for number plate, blur captured image and the image is skew. Based on the results showed in table 4.6, the different types of material and blur image can be segment using bounding box without any distortions. The number plate used different types of material than actual material. The character and the background of number plate is very shinning and it is the modified number plate where the actual height is 5cm. Some of the number plate fails to recognize due to the luminance condition or problematic background of number plates Bounding box method manage to detect the height character and gives correct output in previous

Optical character recognition is the method or process of detection and translation of the printed or written document into an electronic data document and is usually used in data processing systems. Such recognitions requires definite hardware and software tools which can translate the data. [19]

(SIF) matrix with a Back Propagation Neural network (BPN). A GUI based OCR system is

Thus, the future work involves considering textures feature also into consideration along with the geometrical feature and to improve the accuracy and finally the features that are extracted should be selected properly to increase the accuracy.

Pertaining to the tremendous growth of digitalization in the past decade in areas of healthcare, administration, art & commerce and academia, large collections of digital images have been created. Many of these collections are the product of digitizing existing collections of analog photographs, diagrams, drawings, paintings, and prints with which the problem of managing large databases and its repossession based on user specifications came into the picture. Due to the incredible rate, at which the size of image and video collection is growing, it is eminent to skip the subjective task of manual keyword indexing and to pave the way for the ambitious and challenging idea of the contend-based description of imagery.

In order to get the digital copies of printed pictures, segmentation is needed. The main purpose of this work is to analyze how a better and the efficient system can be designed to segments two or more pictures from the same printed document page at the same time. Most of the previous methods for achieving this particular problem is useful only when single image has to segment from the printed document. This particular system will also help us to extract title images and the images in between two pages. For this either digital cameras or flatbed scanners can be used but there is various advantages of digital camera over flatbed scanners such as portability, acquisition without contacting and fast response, whereas the flat scanners requires high computing power.