Accuracy improvement in odia zip code recognition technique

Odia is a very popular language in India which is used by more than 45 million people worldwide, especially in the eastern region of India. The proposed recognition schemes for foreign languages such as Roman, Japanese, Chinese and Arabic can’t be applied directly for odia language because of the different structure of odia script. Hence, this report deals with the recognition of odia numerals with taking care of the varying style of handwriting. The main purpose is to apply the recognition scheme for zip code extraction and number plate recognition. Here, two methods “gradient and curvature method” and “box-method approach” are used to calculate the features of the preprocessed scanned image document. Features from both the methods are used to train the artificial neural network by taking a large no of samples from each numeral. Enough testing samples are used and results from both the features are compared. Principal component analysis has been applied to reduce the dimension of the feature vector so as to help further processing. The features from box-method of an unknown numeral are correlated with that of the standard numerals. While using neural networks, the average recognition accuracy using gradient and curvature features and box-method features are found to be 93.2 and 88.1 respectively

SEVERAL METHODS OF FEATURE EXTRACTION TO HELP IN OPTICAL CHARACTER RECOGNITION

An Optical Character Recognition (OCR) consists of three bold steps namely Preprocessing, Feature extraction, Classification. Methods of Feature extraction yield feature vectors based on which the classification of a testing pattern is executed. The paper aims at proposing some  methods of feature extraction that may go a long way to recognize a Bengali numeral or character. Pixel Ex-OR Method presents a digital gating (Ex-OR) technique to extract the information in an image. Two successive elements of a row in image matrix have been Ex-ORed and the output is again Ex-ORed with the next element.  Alphabetical coding codes a binary character image by means of letters of English alphabet. Directional features find gradient information using Sobel Masks to make position of stroke clear in an image. The features have been derived in eight standard directions and then these eight feature vectors are merged into four sets of features to reduce the system complexity and hence processing time is saved considerably. These features will help develop a Bengali numeral recognition system

Neural Networks for Handwritten English Alphabet Recognition

This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.Comment: 5 pages, 3 Figure, ISSN:0975 - 888

Accuracy Improvement of Off-line Handwritten Tamil Character Recognition

Abstract India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. Tamil is the most popular language in the world and particularly in Tamilnadu. More than 8 crore Tamils live in Tamil Nadu and Pondicherry. About one crore Tamils live in the other states of India. Outside India, Sri Lanka, Burma, Malaysia, Singapore, Indonesia, South Africa, Fiji, Mauritius islands are some of the countries having a large number of Tamil speaking people. Thus, the work on Tamil character is very useful for the Tamil community around the world. This paper deals with the recognition of off-line handwritten Tamil characters. Tamil Character recognition is a most challenging task in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. Off-line handwriting recognition is the subfield of optical character recognition. Here two sets of feature are computed and two classifiers are combined to get higher accuracy of Tamil character recognition. First feature set is computed based on the directional information obtained from the arc tangent of the gradient. Since most of the Tamil handwritten characters have some curve-like parts, curvature-based feature guided by gradient information is computed for the second set of features. Curvature feature detection calculated using bi-quadratic interpolation method. Combined use of Support Vector Machines (SVM) and Modified Quadratic Discriminant Function (MQDF) are applied here for better performance of Tamil character recognition

Investigation of normalization techniques and their impact on a recognition rate in handwritten numeral recognition

This paper presents several normalization techniques used in handwritten numeral recognition and their impact on recognition rates. Experiments with five different feature vectors based on geometric invariants, Zernike moments and gradient features are conducted. The recognition rates obtained using combination of these methods with gradient features and the SVM-rbf classifier are comparable to the best state-of-art techniques

Handwriting recognition using webcam for data entry

The Handwriting Recognition using Webcam for Data Entry project has its primary purpose to develop a system or algorithm that is robust enough to recognize numerical handwritings. A web camera is to be utilized to capture images of handwritten scores and question numbers on the examination score sheet in real time. It is then preprocessed and all the features are being fed into a neural network that is already been trained by various test samples. The outcome of the project should be able to obtain a system that is able to recognize handwritten numerical data with the lowest overshoot and errors. Several distinctive feature from each character is extracted using a few feature extraction methods, in which a comparison between three types of feature extraction modules were used. The first test was done with a neural network trained with only the Character Vector Module as its feature extraction method. A result that is far below the set point of the recognition accuracy was achieved, a mere average of 64.67% accuracy. However, the testing were later enhanced with another feature extraction module, which consists of the combination of Character Vector Module, Kirsch Edge Detection Module, Alphabet Profile Feature Extraction Module, Modified Character Module and Image Compression Module. The modules have its distinct characteristics which is trained using the Back-Propagation algorithm to cluster the pattern recognition capabilities among different samples of handwriting. Several untrained samples of numerical handwritten data were obtained at random from various people to be tested with the program. The second tests shows far greater results compared to the first test, have yielded an average of 84.52% accuracy. As the recognition results have not reached the target of 90%, further feature extraction modules are being recommended and an additional feature extraction module was added for the third test, which successfully yields 90.67%. With the timeframe target achieved, a robust data entry system was developed using web camera together with a user-friendly GUI (Graphical User Interface)

Handwritten Devanagari numeral recognition

Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy

Offline handwritten signature identification using adaptive window positioning techniques

The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn(13x13).This size should be large enough to contain ample information about the style of the author and small enough to ensure a good identification performance.The process was tested with a GPDS data set containing 4870 signature samples from 90 different writers by comparing the robust features of the test signature with that of the user signature using an appropriate classifier. Experimental results reveal that adaptive window positioning technique proved to be the efficient and reliable method for accurate signature feature extraction for the identification of offline handwritten signatures.The contribution of this technique can be used to detect signatures signed under emotional duress.Comment: 13 pages, 9 figures, 2 tables, Offline Handwritten Signature, GPDS dataset, Verification, Identification, Adaptive window positionin