They also provided an offline handwritten gurmukhi character recognition. A novel feature extraction technique is presented in this paper for an offline handwritten gurmukhi character recognition system. For the purpose of training and testing data set, we have collected around 10,500 samples of isolated offline handwritten gurmukhi characters. The character set of traditional handwritten gurmukhi script documents contains symbols that are not used in modern gurmukhi script. Offline handwritten gurmukhi character recognition proceeding of.
The system first prepares a skeleton of the character, so that feature information about the character is. Recognition using different feature sets and classifiers a survey. Svm based offline handwritten gurmukhi character recognition. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network. An analytical study of handwritten character recognition. Performance analysis of zone based features for online. This paper represent handwritten gurmukhi character recognition system using some statistical features like zone density, projection histograms, 8 directional zone density features in combination with some geometric features like area, perimeter, eccentricity, etc. The knearest neighbor, support vector machine and probalistic neural network are used as classifier. Recognition of handwritten characters is a difficult task owing to various writing styles of individuals. Mainly, character recognition machine will takes the raw data that for further implements.
For offline handwritten gurmukhi character recognition two approaches are reported. This paper may give the significance of an offline handwritten character recognition system in various applications, and may help to give different. Comparison between neural network and support vector. On the basis of data acquisition process, character recognition system can be classified into following categories. Handwritten character recognition, feature extraction, diagonal features, intersection and open end points features, svm. There are lots of touching characters in a single word. Pcabased offline handwritten character recognition system.
In this thesis work we have proposed offline recognition of isolated handwritten characters of gurmukhi script. The process of converting scanned images of handwritten text converted to machine understandable format is termed as offline handwritten character recognition, whereas online handwritten character recognition is the process of converting pentip movements in the form of. Recognition of handwritten characters is a difficult. This paper described seven applications based on offline handwritten characters recognition system. Handwritten gurmukhi character recognition using statistical. Offline handwritten gurmukhi character recognition using. Principal component analysis pca has also been used for extracting representative features for character recognition.
In offline handwriting recognition, prewritten data generally written on a sheet of paper is scanned. The extracted features were then fused together to. Variations in handwriting are one prominent problem and achieving high degree of accuracy is a tedious task. Pdf online handwritten gurmukhi character recognition. For the purpose of classification, we have used knn, linearsvm, polynomial svm and rbfsvm based approaches. This paper presents an experimental assessment of the effectiveness of various. Khmer language has been identified as one of the most complex language with the total of 74 alphabets and the wording compound can has up to 5 vertical levels. In this paper, we deal with wekabased classification methods for offline handwritten gurmukhi character recognition. Offline handwritten presegmented character recognition of. In this paper, we deal with weka based classification methods for offline handwritten gurmukhi character recognition. Table 1 indicated the summarized results obtained so far in handwritten devanagari character recognition. The offline handwritten character recognition is the frontier. Recognition of isolated handwritten characters is the process. In our work we have considered 35 basic characters of gurmukhi script all assumed to be isolated and bearing header lines on.
A dataset of online handwritten assamese characters. Support vector machines svms have successfully been used in recognizing printed characters. The proposed work depends on the handwriting word level, and it does not need for character segmentation stage. Online and offline handwritten chinese character recognition. Pdf k nearest neighbour based offline handwritten gurmukhi.
In that work, they performed recognition without using pca and used only an svm classifier for classification purpose. The recognition rate obtained in the case of online handwritten assamese numerals is higher than the recognition rate of 96. Handwritten digit recognition using support vector machine. Jul 18, 2014 these techniques require good quality features as their input for the recognition process. This paper proposes one new method, svm for khmer character classification. In handwritten recognition, svm gives a better recognition result. In this paper, we have proposed two different feature extraction techniques, namely, parabola curve fitting based features and power curve fitting based features for offline handwritten gurmukhi character recognition.
Handwritten character recognition has been broadly classified in to two types. Online and offline character recognition and offline character recognition further divided. We only consider isolated handwritten chinese character recognition in this study since it is still an unsolved problem, while the handwritten text recognition will be considered indepth in other works. In case of offline character recognition, the typedhandwritten characters are scanned and then converted into binary or gray scale image. Offline handwritten gurmukhi character recognition using particle swarm optimized neural network. Pdf offline handwritten gurmukhi character recognition using. Offline handwritten gurmukhi character recognition. The system first prepares a skeleton of the character, so that feature information about. Benchmark datasets for offline handwritten gurmukhi script. In online handwriting recognition, data is captured during the writing process with the help of a special pen on electronic surface. Handwritten character recognition has applications in postal code recognition, automatic data entry into large administrative systems, banking, digital libraries and invoice and receipt processing. To select a set of features is an important step for implementing a handwriting recognition system. A little more detailed survey on gurmukhi recognition is presented in 6 and 19. Optical character recognition, particle swarm optimization, handwriting recognition, gurmukhi characters, artificial neural network, handwritten character recognition.
Dwt2 has also been considered with three different types, namely, haar wavelet, daubechies db 1. Since late sixties, efforts have been made for offline handwritten character recognition throughout the world. Then the svm is used to estimate global correlations and classify the pattern. Indian character recognition and many recognition systems for. Efficient feature extraction techniques for offline. A scheme for offline handwritten gurmukhi character recognition based on svms is presented by munish, jindal and sharma 2011. The main goal of this thesis is to develop an online handwritten gurmukhi character recognition system. Here two sets of features based on gradient and curvature of character image are computed. As a result of advances in optical character recognition research, several techniques for handwritten character recognition have surfaced. The preprocessing stage reduces noise and distortion, removes skewness and performs skeletonization of the image. This paper presents a comparative study of various classifiers and the results achieved for offline handwritten. In case of offline character recognition, the typed handwritten characters are scanned and then converted into binary or gray scale image.
Recognition of isolated handwritten characters in gurmukhi. Anoop rekha 4 has presented a complete survey on different feature sets and classifiers used in offline handwritten gurumukhi character and numeral recognition. Devanagari and gurmukhi script recognition in the context. Gurmukhi is the script of punjabi language which is widely spokenacross the globe. This paper represent handwritten gurmukhi feature recognition. These techniques require good quality features as their input for the recognition process. Recognition accuracy based on svm with polynomial kernel. Moment invariant and affine moment invariant techniques are used as feature extraction. Offline handwritten gurmukhi character and numeral recognition. Introduction optical character recognition ocr is a technique that allows convertingthe printed text into an editable format in computer. The system first prepares a skeleton of the character, so that feature information about the character is extracted. Optical character recognition, support vector machine, artificial neural network 1.
In order to assess the prominence of features in offline handwritten gurmukhi character recognition, we have recognized offline handwritten gurmukhi characters with different combinations of features and classifiers. Handwriting recognition is in research for over four decades and has attracted many researchers across the world. A novel feature extraction technique for offline handwritten. Handwritten character recognition is a complex task because of various writing styles of different individuals. Pdf handwritten digit recognition using support vector. Offline handwritten character recognition ohcr is the method of converting handwritten text into machine processable layout. Pdf pca based offline handwritten gurmukhicharacter. In the present work, we have used this classification technique to recognize handwritten characters. As shown in table 1, the maximum accuracy obtained in case of handwritten devanagari numerical recognition 22 is 95. The languages on which massive work has been done are biographical notes. Then feature extraction and recognition process is carried over the binary image. International journal of information technology and computer science 6 2, 2014.
The variability of writing styles, both between different. Sometimes more than two characters touch each other, making the algorithm process more complicated. Machine svm based classification method on khmer printed characterset recognition pcr in bitmap document. In this paper, we have presented an offline handwritten gurmukhi character recognition system using various transformations techniques, namely, discrete wavelet transformations dwt2, discrete cosine transformations dct2, fast fourier transformations and fan beam transformations. Support vector machine svm based classifier for khmer. The system first prepares a skeleton of the character. Pdf svm based offline handwritten gurmukhi character. From among the three svm kernels used in this experiment for assamese alphabetic characters, the rbf kernel gives the best recognition rate of 81. Nov 18, 2014 the increasing need of a handwritten character recognition system in the indian offices such as banks, post offices and so forth, has made it an imperative field of research. Combination of different feature sets and svm classifier. Gurmukhi printed character recognition using hierarchical. Character recognition is a process of conversion of an image of a handwritten or printed text in to a computer editable format.
Handwriting recognition is a technique that convert handwritten characters into machine processable formats. Word segmentation and character segmentation is used as segmentation stages. Isolated curved gurmukhi character recognition using. A offline handwritten gurmukhi character recognition based on k recognition. Pdf offline handwritten character recognition techniques.
Support vector machine svm is an alternative to nn. Pdf offline handwritten gurmukhi character recognition. Handwriting word recognition based on svm classifier. Thus, this study provides a benchmark of online and offline handwritten chinese character recognition on the new standard datasets. An arabic handwriting dataset ahdb, dataset used for train and test the proposed system. Handwritten gurmukhi numeral recognition using zonebased.
Offline character recognition is a more challenging and difficult task as there. Sharma3 1assistant professor, computer science department, ggs college for women, chandigarh, india 2associate professor, department of computer science and applications, panjab university regional centre, muktsar, india. In present paper, authors have presented a novel hierarchical technique for isolated offline handwritten gurmukhi character recognition. We have also extended the work by applying the same methodology to recognize handwritten gurmukhi numerals. Pdf weka based offline handwritten gurmukhi character. The increasing need of a handwritten character recognition system in the indian offices such as banks, post offices and so forth, has made it an imperative field of research. Kumar et al pcabased offline handwritten character recognition system 348 in this phase, the graylevel character image is nor malized into a window sized 1 00. Line segmentation of handwritten gurmukhi manuscripts. The recognition of handwriting can, however, still is considered an open research problem due to its substantial variation in appearance. Performance evaluation of classifiers for the recognition. In this paper, we have proposed two different feature extraction techniques, namely, parabola curve fitting based features and power curve fitting based features for. Handwritten character recognition is mainly of two types online and offline. A scheme for offline handwritten gurmukhi character recognition based on svms is presented in this paper. For the purpose of classification, we have used knn, linearsvm, polynomialsvm and rbfsvm based approaches.
Svm classifiers concepts and applications to character. Structural features generally provide better results for the handwritten symbol recognition. Role of offline handwritten character recognition system. Machine svm based classification method on khmer printed character set recognition pcr in bitmap document.
A novel hierarchical technique for offline handwritten. Offline handwritten gurmukhi word recognition using deep. Various methods are analyzed that have been proposed to realize the core of character recognition in an optical character recognition system. A robust feature set of 105 feature elements is proposed under this work for. Abed 8 presented an overview on handwritten character. Based on the learning adaptability and capability to solve complex computations, classifiers are always the best suited for the pattern recognition problems. This paper deals with the offline recognition of handwritten gurmukhi characters. Handwritten gurmukhi numeral recognition using zone. A brief outline of each chapter is given in the following paragraphs.
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