this paper a support vector machine based handwritten numerals recognition system of Manipuri Script (Meetei Mayek) is investigated. We have used various feature extraction technique such as background directional distribution (BDD), zone based diagonal, projection histograms and Histogram Oriented Gradient features. In Background Directional Distribution (BDD) features background distribution of neighboring background pixels to foreground pixels in 8-different directions is considered forming a total of 128 features. For the computation of diagonal features, the whole image is divided into 64 zones of equal dimension each of size 4×4 pixels then features are extracted from the pixels of each zone by moving along the diagonal, thus consisting of 64 features in total. Projection Histograms count the number of foreground pixels in different directions such as vertical, horizontal, horizontal, left diagonal and right diagonal creating a total of 190 features. The HOG based feature is computed over the validation data set, was achieved by means of 9 rectangular cells and 9 bin histogram per cell Different combinations of these features are used for forming various feature vectors. These feature vectors are classified by using SVM classifier as 5-fold cross validation with RBF (radial basis function) kernel. Experimental results show that the proposed system performs well with the combined features and is robust to the writing variations that exist between persons and for a single person at different instances, thus being promising for user independent recognition of Meetei Mayek numeral.

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Recognition of Handwritten Numerals of Manipuri Script

In recent years, many information retrieval, character recognition, and feature extraction methodologies in Devanagari and especially in Hindi have been proposed for different domain areas. Due to enormous scanned data availability and to provide an advanced improvement of existing Hindi automated systems beyond optical character recognition, a new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic is introduced. This first pre-processes and then classifies textual imaged documents into predefined categories. With this concept, this article depicts a feasibility study of such systems with the relevance of Hindi, a survey report of statistical measurements of Hindi keywords obtained from different sources, and the inherent challenges found in printed and handwritten documents. The technical reviews are provided and graphically represented to compare many parameters and estimate contents, forms and classifiers used in various existing techniques.

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

Intelligent Character Recognition (ICR) is a specific form of optical character recognition (OCR) dealing mostly with handwritten texts Due to their specificity, they are usually more adept in interpreting different styles and fonts of handwriting providing eventually higher recognition rates Factors like language constructs, amount of research on ICR pertaining to the language, etc, essentially determines the amount of success achieved in its character recognition This research mainly deals with the recognition of Gujarati Handwritten Characters We have considered 34 consonants and 5 vowels; a total of 39 Gujarati Characters The structure and lexicons of the language posed a challenge during the initial phase of segmentation; for that we have proposed new algorithm for segmentation Our segmentation algorithm is able to address these concerns effectively Different algorithms from different domains have been considered for comparative analysis like Transform Domain (DWT, DCT and DFT), from Spatial Domain; Geometric Method (Gradient feature), Structural method (Freeman chain code) and Statistical method (Zernike Moments) We have also proposed a new Combination of Structural and Statistical methods (Freeman chain code, Hu's invariant moment and center of mass) to extract feature vectors and it results into good amount of accuracy These extracted feature vectors were further supplied as input into Support Vector Machines and their resulting accuracies were analyzed using 10 fold cross validation SVM performs well on data sets that have many attributes and can also handle large number of classes

Classification of offline gujarati handwritten characters

Offline handwritten character recognition is the process of recognizing given characters from the large set of characters. OCR system mainly focuses on the recognition of printed or handwritten characters of a scanned image. The proposed system extracts features that are based only on gradient of image which is helpful in exact recognition of characters. A technique to recognize handwritten Devanagari characters using combination of quadratic and SVM classifiers is presented in this paper. Features used are directional features that are strength, angle and histogram of gradient (SOG, AOG, HOG). Using a Gaussian filter, the strength and the angle features are down sampled to obtain a feature vector of 392 dimensions. These features are finally concatenated with HOG feature. Applying these to the combination of quadratic and SVM classifiers to obtain maximum accuracy of 95.81% using 3 fold cross validation.

Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features

For development of offline Handwritten Character Recognition (HWCR) system, scripts have always posed a difficulty. In the proposed system, we present neural and non-neural approach for classification of different characters. After pre-processing, features are extracted using Chain code histogram and Intersection junction techniques. BPN, KNN & SVM have been used to train and classify the Devnagari and MODI vowels separately. The combinations have been compared for both the scripts on the basis of their recognition rate.

Performance analysis of handwritten Devnagari and MODI Character Recognition system

In this paper, we present a methodology for off-line Isolated handwritten Devanagari character recognition The proposed methodology relies on a three feature extraction techniques The first technique is based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible Second technique is based on the zone density of the pixel and third is based on the directional distribution of neighboring background pixels to foreground pixels The 314 sized feature vector is form from the three feature extraction techniques for a handwritten Devanagari character The dataset (12240 samples) of handwritten Devanagari Character, have been prepared by writing the different - 2 people who belongs to different age group and obtained the 9489 % recognition accuracy

Devanagari Isolated Character Recognition by using Statistical features ( Foreground Pixels Distribution, Zone Density and Background Directional Distribution feature and SVM Classifier)

Mahesh Jangid

Papers

Devanagari Isolated Character Recognition by using Statistical features ( Foreground Pixels Distribution, Zone Density and Background Directional Distribution feature and SVM Classifier)