Vision based System for Optical Number Recognition (original) (raw)
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Optical character recognition by the method of moments
Computer Vision, Graphics, and Image Processing, 1987
An investigation of the use of two-dimensional moments as features for recognition has resulted in the development of a systematic method of character recognition. The method has been applied to six machine-printed fonts. Documents used to test the method contained 24 lines of alphanumeric characters. Before scanning a document to be processed, a training document having the same font must be scanned and stored in memory. Characters on the training document are isolated by contour tracing, and then the 2D moments of each character are computed and stored in a library of feature vectors. The document to be recognized is then scanned, and the 2D moments of its characters are compared with those in the library for classification. In this paper we present the selection of a set of moments that provide good discrimination between characters, the comparison of three classification schemes, the selection of a weighting vector that improves the classification performance, and a series of experiments to determine how the recognition rate is affected by the number of library feature vector sets. Recognition rates between 98.5% and 99.7% have been achieved for all fonts tested.
Effective optical processor for computing image moments at TV rate: use in handwriting recognition
Applied Optics, 1987
An incoherent multiplexed optical processor is described that computes in real time the moments of images sensed by a TV camera and displayed on a monitor. The first ten moments are generated in parallel and are detected by a photodetector array. They are collected by a microcomputer for subsequent pattern recognition use. Calibration and resolution problems are discussed with regard to the effective 2% system accuracy.
Optical Character and Digit Recognition System
2020
1-2Student, Dept. of Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Since olden times, the need for storing information in various ways has always been there. This was very useful until we felt the need to reuse this information again and again. In request to reuse these snippets of data, we had to read and search individual contents from different documents and then rewrite it again. Thus, there is an explicit need for automated softwares or programs in order to provide fast and accurate methods to revive the text from the longlasting images and documents.
Automatic Numeral Recognition System Using Local Statistical and Geometrical Features
Iraqi journal of science, 2023
Optical Character Recognition (OCR) research includes computer vision, artificial intelligence, and pattern recognition. Character recognition has garnered a lot of attention in the last decade due to its broad variety of uses and applications, including multiple-choice test data, business documents (e.g., ID cards, bank notes, passports, etc.), and automatic number plate recognition. This paper introduces an automatic recognition system for printed numerals. The automatic reading system is based on extracting local statistical and geometrical features from the text image. Those features are represented by eight vectors extracted from each digit. Two of these features are local statistical (A, A th), and six are local geometrical (P 1 , P 2 , P 3 , P 4 , P 5 , and P 6). Thus, the database created consists of 1120 statistical and geometrical features. For the purpose of recognition, the features of the test image are compared with the features of all the images saved in the database depending on the value of the Minimum Distance (MD). All digits (0-9) were identified with 100% accuracy. The average computational time required to recognize a numeral at any font size is 0.06879 seconds.
Implementing a System for Recognizing Optical Characters
In the current paper we present a system of characters recognition by taking the photo of character with the identity of symbolic. In the proposed system we are going to make a scan in kind of optical for input character in order to be digitized. After that every character will be segmented and located and after that it will be obtained as a photo to be processed for normalization and even for reducing noise. After that it will be classified. Then from the obtained extraction we can find various techniques like weakness and strengths. Next step will be grouping the characters which identified in order to obtain the original string of symbols and we can apply the context in order to fix and detect false. The results show us that the system is working well and the recognition is really good. The system proposed in a program, developed in Matlab environment, which provides the ability to insert a character in an image. It is agree that making a machine to do what human can do is a dream, for example reading is one of the most important functions that humans are doing. However, this dream is becoming true day by day and researchers and working on this by many ways, where nowadays artificial intelligence is focusing on pattern recognition and in this field it is also focusing on the applications of character recognition and even many organizations and companies are designing systems for character recognition by many application and even that it is facing some challenges to make machines be able to read like humans and have the same capabilities. Recognizing characters is challenging some problems with the optical characters. Although, it is performed to be off line optical recognition for characters especially after completing the printing and writing, and to be online recognition to recognize characters as they have been drawn or written. Printed characters and even hand written characters could be recognized, but what we are always looking for is the performance where especially it is depending on the quality of files that been entered. Next step of challenging reviewed by many researchers is the online and the offline cursive writing. To get new ideas in the recognition of pattern, the classifying of characters could be tested, but where the experiments results are conducted on isolated characters, here the results are not necessary in case of immediately relevant to optical character recognition. Maybe more striking than the improvement of the accuracy and limit in methods of classification has been decreased in cost. The old devices of optical character recognition equipments were some optical hardware like the optical page reader of the company of IBM in order to read typed earning reports at the social security administration which cost more than two million dollars and some electronic and some high expensive scanners. Nowadays, the software of optical character recognition is often add on to scanner of desktop which is not costly. The main goal is to examine some details in examples of the false which committed by the proposed system. 2. PROPOSED SYSTEM The general technique is very simple to describe. The proposed optical character recognition system will contain some components and they are presented in figure 1. The install is illustrated, where to digitize the analog file by the optical scanner will be the first step in the system. After that the area which containing characters will be located and every symbol extracted by the process of segmentation. After that applying a preprocessing on the extracted symbols and then we are going to reduce the noise and eliminate it in order to make it easier the feature extraction to be prepared for the coming step. After that we are going to comparing the description of the classes of symbols which are gained by a phase of previous learning with the extracted features in order to find the identity of the symbol. Then to reconstruct the numbers and words of the original string we are going to use the contextual information.
Optical Character Recognisation
Optical Character Recognition by using Template Matching is a system which is useful to recognize the character or alphabets in the given text by comparing two images of the alphabet. The objectives of this system prototype are to develop a program for the Optical Character Recognition (OCR) system by using the Template Matching algorithm . This system has its own scopes which are using Template Matching as the algorithm that applied to recognize the characters, which are in both in capitals and in small (A – Z),and the numbers (0 -9) used with courier new font type, using bitmap image format with 240 x 240 image size and recognizing the alphabet by comparing between images which are already stored in our database is already . The purpose of this system prototype is to solve the problems of blind peoples who are not able to read , in recognizing the character which is before that it is difficult to recognize the character without using any techniques and Template Matching is as one of the solution to overcome the problem
Number Recognition from Captured Images
International Journal of Computer Applications, 2016
Now-a-days because of digitization, it is very important to have all the data in the form of soft copy so that it is easy to store and maintain. Often the handwritten documents are available, but again if we want to use the data written in that document then we need to type that data in any word processing software and then use it. This is very time consuming and tedious job. In this paper an attempt is made to automatically recognize numbers in the document by capturing images of that document. Till now emphasis is given only on recognizing digits or numbers from images. This methodology is implemented in automatic marks filling system. This system captures images of the tables in which marks are written by the teacher in the students" answer book, recognize the numbers or marks from it and fill the same in appropriate database on the computer. The fundamental technique used here is Dynamic Time Warping (DTW). Although this technique is used for speech processing, this paper describes its exclusive use in number recognition from its images. The system is implemented and simulated in MATLAB.
Printed and Handwritten Hindi/Arabic Numeral Recognition Using Centralized Moments
International Journal of Scientific and Engineering Research
Printed and handwritten numerals recognition plays a vital role in postal automation services. The major problem in handwritten recognition is the huge variability and distortions of patterns. The aim of the current research work is to develop fast and efficient method to recognize Hindi printed and free handwritten numerals objects. In this research, the introduced method for extracting features from patterns is based on the relative density distribution of each numeral object; specifically it depends on the centralized moments. This method gives sufficient results to recognize the printed and highly stylized handwritten numeral images. The attained recognition rate is 97.47% for the printed numeral images with total number of samples equal (198) samples and 95.55% for the highly stylized handwritten numeral images with total number of samples equal (90) samples, while, the attained recognition rate is unacceptable when the system is applied for a handwritten numeral samples which ...
2014
This paper presents a simple and effective Optical Character recognition system (OCR) for accurate detection of digits. Initially 0-9 digits are classified into four groups using the background pixel range. After detection of the group, the digits are distinctly identified using intrinsic ratio as mathematical parameter. Furthermore any overlapping digits are recognized using curved contour coordinates. Experimenting with a large data set we have extracted the exact range of all the parameters used for recognition. Recognition results and lucid flow reveals simplicity of the algorithm. Keywords— Simple OCR , Digit recognition , Digit OCR , OCR Algorithm
Character recognition using statistical moments
Image and Vision Computing, 1999
This paper presents a character recognition system that is implemented using a variety of statistical moments as features. These moments include Hu moment invariants, Affine moment invariants and Tsirikolias-Mertzios moments. Euclidean distance measure, cross correlation and discrimination cost were used as the classification techniques. The mean of the intraclass standard deviations of the features was used as a weighting factor during the classification process to improve recognition accuracy. The system was rigorously tested under different conditions, including using different number of training sets and documents with different fonts. It was found that Tsirikolias-Mertzios moments with weighted cross correlation classifier provided the best recognition rates. ᭧