Rajneesh Rani

Script Identification of Pre-segmented Multi-font Characters and Digits

Character recognition problems of distinct scripts have their own script specific characteristics. The state-of-art optical character recognition systems use different methodolgies, to recognize different script characters, which are most effective for the corresponding script. The identificaton of the script of the individual character has not brought much attention between researchers, most of the script identification work is on document, line and word level. In this multilingual/multiscript world presence of different script characters in a single document is very common. We here propose a system to encounter such adverse situation in context of English and Gurumukhi Script. Experiments on multifont and multisized characters with Gabor features based on directional frequency and Gradient features based on gradient information of an individual character to identify it as Gurumukhi or English and also as character or numeral are reported here. Treating it as four class classification problem, multi-class Support Vector Machine(One Vs One) has been used for classification. We got promising results with both types of features. The average identification rates obtained with Gabor and Gradient features are 98.9% and 99.45% respectively.

Performance Comparison of Devanagari Handwritten Numerals Recognition

In this paper an automatic recognition system for isolated Handwritten Devanagari Numerals is proposed and compared the recognition rate with different classifier. We presented a feature extraction technique based on recursive subdivision of the character image so that the resulting sub images at each iteration have balanced numbers of foreground pixels as possible. Database, provided by Indian Statistical Institute, Kolkata, have 22547 grey scale images written by 1049 persons and obtained 98.98% highest accuracy with SVM classifier. Results are compared with KNN and Quadratic classifier.

Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

Recognition of Indian languages is a challenging problem. In Optical Character Recognition (OCR), acharacter or symbol to be recognized can be machine printed or handwritten characters/numerals. Several approaches in the past have been proposed that deal with problem of recognition of numerals/character depending on the type of feature extracted and way of extracting them. In this paper also a recognition system for isolated Handwritten Devanagari Numerals has been proposed. The proposed system is based on the division of sample image into sub-blocks and then in each sub-block Strength of Gradient is accumulated in 8 standard directions in which Gradient Direction is decomposed resulting in a feature vector with dimensionality of 200. Support Vector Machine (SVM) is used for classification. Accuracy of 99.60% has been obtained by using standard dataset provided by ISI (Indian Statistical Institute) Kolkata. General Terms Pattern Recognition, Indian Scripts, Optical Character Recognition.

Handwritten Gurmukhi Numeral Recognition using Zone- based Hybrid Feature Extraction Techniques

This paper presents an overview of Feature Extraction techniques for off-line recognition of isolated Gurumukhi numerals/characters. Selection of Feature Extraction method is probably the single most important factor in achieving high performance in pattern recognition. Our paper presents Zone based hybrid approach which is the combination of image centroid zone and zone centroid zone of numeral/character image. In image centroid zone character is divided into n equal zone and then image centroid and the average distance from character centroid to each zones/grid/boxes present in image is calculated. Similarly, in zone centroid zone character image is divided into n equal zones and centroid of each zones/boxes/grid and average distance from zone centroid to each pixel present in block/zone/grid is calculated. SVM for subsequent classifier and recognition purpose. Obtaining 99.73% recognition accuracy. General Terms Feature extraction method, Image centroid zone, zone centroid zone, support vector classifier (SVM)

Combination of Different Feature Sets and SVM Classifier for Handwritten Gurumukhi Numeral Recognition

A lot of research has been done in recognizing handwritten characters in many languages like Chinese, Arabic, Devnagari, Urdu and English. This paper focuses on the problem of recognition of isolated handwritten numerals in Gurumukhi script. We have used different feature extraction techniques such as projection histograms, background directional distribution (BDD) and zone based diagonal features. Projection Histograms count the number of foreground pixels in different directions such as horizontal, vertical, left diagonal and right diagonal creating 190 features. In Background Directional Distribution (BDD) features background distribution of neighbouring background pixels to foreground pixels in 8-different directions is considered forming a total of 128 features. In the computation of diagonal features, image is divided into 64 equal zones each of size 4×4 pixels then features are extracted from the pixels of each zone by moving along its diagonal, thus consisting of total 64 features. Different combinations of these features are used for forming different feature vectors. These feature vectors are classified using SVM classifier as 5-fold cross validation with RBF (radial basis function) kernel. The highest accuracy achieved is 99.4% of whole database using combination of background directional distribution and diagonal features with SVM classifier. General Terms Pattern Recognition, OCR, Handwritten Character Recognition, Feature Extraction.

Comparative Analysis of Gabor and Discriminating Feature Extraction Techniques for Script Identification

A considerable amount of success has been achieved in developing monolingual OCR systems for Indian Scripts. But in a country like India, where many languages and scripts exist, it is more common that a single document contain words from more than one script. Therefore a script identification system is required to select the appropriate OCR. This paper presents a comparative analysis of two different feature extraction techniques for script identification of each word. In this work, for script identification discriminating and Gabor filter based features are computed of Punjabi words and English numerals. Extracted feature are simulated with Knn and SVM classifiers to identify the script and then recognition rates are compared. It has been observed that by selecting the appropriate value of k and appropriate kernel function with appropriate combination of feature extraction and classification scheme, there is significant drop in error rate.

SVM classifier for recognition of handwritten devanagari numeral

In this manuscript we recognize the handwritten Devnagari numerals. In our implementation we have used density and background directional distribution features for the zones, in which we divided the numeral samples already. We used the normalized images of samples of varying sizes of 32*32, 40*40 and 48*48. We divided these normalized images into 4*4 (16), 5*5 (25) and 6*6 (36) zones respectively to compute the features for each zone. In all the cases each zone is of size 8*8 pixels. Each zone contains 9 features consisting of one density feature and 8 backgrounds directional distribution features. The zonal density feature is computed by dividing the number of foreground pixels in each zone by total number of pixels in the zone i.e. 64. The other 8 features are based on directional distribution values of background in eight directions. These directional values are computed for each foreground pixel by summing up the value corresponding to neighbouring background pixels given in the specific mask for each direction. For each direction these directional distribution features are summed up for all pixels in each zone. Thus numbers of features finally used for recognition are 144, 225 and 324 for samples of respective sizes in increasing order. For classification purpose we have used SVM classifier with RBF kernel. Our dataset of handwritten Devnagari numerals used is provided by Indian Statistical Institute (ISI), Kolkata. Training data size is 18783 and testing data size is 3763, totally 22546. The optimum 5-fold cross validation accuracies of training data obtained for varying sizes of samples in increasing order are 98.76%, 98.91% and 98.94% respectively. By observing the cross validation results it is conclusive that at the cost of increasing the features size there is only minute increases in the performance. So we recommend 144 sized feature vector to recognize testing samples. The testing accuracy by using 144 features for 32*32 normalized samples observed is 98.51% which is prominent and cost-efficient.

Performance analysis of feature extractors and classifiers for script recognition of English and Gurmukhi words

Script Recognition is a challenging field for the recognition of documents in a multilingual country like India where different scripts are in use. For optical character recognition of such multilingual documents, it is necessary to separate blocks, lines, words and characters of different scripts before feeding them to the OCRs of individual scripts. Many approaches have been proposed by the researchers towards script recognition at different levels (Block, Line, Word and Character Level). Normally Indian documents, in any its state language contain English words mixed with other words in its own state language. In this paper, we extract three different types of features: Structural, Gabor and Discrete Cosine Transforms(DCT) Features from Isolated English and Gurmukhi words and compare their script recognition performance using three different classifiers: Support Vector Machine (SVM), k-Nearest Neighbor and Parzen Probabilistic Neural Network (PNN).

Pattern generation and symmetric key block ciphering using cellular automata

This paper presents a symmetric key block cipher technique using Cellular Automata (CA). The proposed system deals with theory and application of cellular automaton. Cellular automata have the property of state transition that is basis to define fundamental transformations for encryption and decryption in the enciphering system. Firstly, the patterns are generated using MATLAB (v2011) and then the cryptographic system is implemented in C language but it can be implemented in other languages too. Different rule configurations of CA are used to form hybrid reversible cellular automata to be used in encryption and decryption of the data in the form of string or character array.

Performance analysis of different classifiers for recognition of handwritten Gurmukhi characters using hybrid features

The paper is focuses on using hybridization of multiple features with different classifiers for the purpose of recognition of isolated handwritten Gurmukhi character images. We have tested four different types of features named as Histogram Oriented Gradient (HOG), Distance Profile, Background Directional Distribution (BDD) and Zonal Based Diagonal (ZBD). HOG feature is computed by information of Directions provided from gradients tangent of arc. Distance Profile can be computed by counting pixels from bounding line of image of character to edge of character from different directions. BDD feature can be computed by background distribution of foreground pixels to background pixels in eight different directions. For computation of ZBD feature, image is segmented into 100 equal zones then feature is calculated from pixels of each zone by traveling along its diagonal direction. For these experiment seven thousand isolated images of Gurmukhi characters have been tested. The experiment achieves a maximum recognition accuracy of 97.257% with 5-fold and 97.671% with 10-fold cross validation by applying hybrid features on SVM classifier.