Devanagari Research Articles

Quality of life improvement with rehabilitation according to constitution of the World Health Organization for coronary artery bypass graft surgery patients: A descriptive review.

This is a descriptive review focusing on trends of treatments required for postoperative coronary artery bypass graft surgery (CABG) patients to improve the quality of life (QOL).Methodology:The sources of literary research to understand the concepts of coronary artery disease according to Indian scriptures are Ayurveda texts, Bhagavad Gita, Patanjali Yoga Sutra. The data was typed in Sanskrit using Devanagari script and explanation in English was given. As per new research techniques, surgery, physiotherapy rehabilitation and Yoga are serving CABG patient's medical and psychological health better. The World Health Organization (WHO) defines health as physical, mental and social well-being later redefined with additional terms like environmental and spiritual health. This definition is similar to the Panchakosha concept in Yoga and Pancha Mahabhutas in Ayurveda. In cases of emergency or passive treatment, medication serves as a better option for physical health. In circumstances where the person is able to move in daily activities (just after discharge), rehabilitation serves as a better option for physical, mental and social health. Travel and reactions to climatic change serve environmental health. Last strategy, belief, cultural and traditional methods with scientific background serves as the spiritual health. These step-wise treatments are required for CABG patients to get the overall health or QOL. However, surgery and physiotherapy rehabilitation are advanced as per modern era which serves physical, mental, and social health also, but environmental health and spiritual health have yet to be addressed. As an ancient system of medicine, Yoga combines physical, mental, social, environmental and spiritual practices and it should be added as treatment along with surgery and physiotherapy rehabilitation. If all of these therapies were in the treatment protocol for CABG surgery patients, we would observe the changes of QOL and fulfill the requirements of constitution of the WHO. Integrating concepts of Yoga, Ayurveda, modern rehabilitation, surgery and patient cooperation with lifestyle change are the key to QOL improvements after CABG.

Multi-objective binary DE algorithm for optimizing the performance of Devanagari script-based P300 speller

P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliability in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.

Automatic Text Correction for Devanagari OCR

Objectives: This paper proposes a new technique for correcting errors done by Devanagari OCR (Optical Character Reader) system based on confusion matrix. Methods/Statistical Analysis: Confusion matrix is generated from large corpus of Hindi. The system takes each word of OCR output and generate number of strings from topmost five confused characters for each character of input word along with probability of these strings for ranking. Each string is validated with the character trigram dictionary and these valid strings are used for best suggestions. Findings: The topmost five words is taken as suggestions. The system has been tested for variety of OCR outputs documents of Devanagari script. The system provides suggestions for all the correct words at top position. For more than 10000 unique words in Devanagari OCR output, system gives the accuracy of 97%. Application/Improvements: This system is used in post-processing of Devanagari OCR. With some improvements, the system can also be used for Gurumukhi Script and Urdu script.

Novel Geometrical Shape Feature Extraction Techniques for Multilingual Character Recognition

ABSTRACTMultilingual character recognition from the images of aged Indian documents is challenging because of the complex character grapheme of the Indian language scripts. Feature extraction plays the most important role in recognition of such images. In this paper, we have proposed a set of feature vectors (FVs) which are based on shape geometry (SG) decoding of the input character. The first FV is based on SG decoding of the input character using triangular area (TA) calculation. The second FV, namely, SG using perpendicular distance is extracted by dividing the input image into individual components and the shape of the individual component is decoded into shape symbols by comparing the normalized perpendicular distances of the individual pixels of the component onto the line joining the end points of the component. Apart from the proposed FVs, we have used crossing count features. These FVs are represented as the string of shape operators; hence, we have used minimum edit distance classifier to recognize the input character. The proposed character recognition technique is evaluated using the characters extracted from printed aged multilingual Indian documents having English, Devanagari, and Marathi scripts and achieved encouraging results. To further assess the performance of the proposed system, we have considered publicly available media-lab license plate benchmark database and achieved significant performance.

The Holy Basil (<I>Ocimum sanctum</I> L.) and its Genome

Ocimum sanctum L. (O. tenuiflorum) is popular in India as “Holy basil”. It is not only designated as sacred basil but is also used as an important ingredient in Indian and some international traditional medicinal systems. There are many theories about the origin of “Tulsi” in ancient Hindi scripts. This herb is a repository of medicinal compounds and thus is also used in treatment of various diseases and disorders, but the validation of phytomolecules with differential therapeutic activities is still a challenge. Approximately, 80% of the patents on Ocimum sp. are on the chemical extracts or the utility of plant parts with main focus on essential oil components. Thus, an effort towards recognition of full metabolic potential of this sacred herb, sequencing of the whole nuclear and chloroplast genomes was performed. The data generated was obtained by combining the sequence reads from 4 libraries of three NGS platforms. This data resulted in a saturated genome draft assembly of about 386 Mb. Pathway analysis from the data indicated the dominance of phenylpropanoids in O. sanctum. Comparison of the available genes and the chemical compounds in O. sanctum and its nearest neighbor Salvia miltiorrhiza, revealed the prospects of discovering new active molecules. This information generated will help in providing new insights relating to the medicinal nature of the metabolites synthesized in the plant.

DEVNAGRI SCRIPT RECOGNITION USING ARTIFICIAL NEURAL NETWORK CLASSIFIER

In India, Devnagari script is used by 500 million people and more. Now-a-days Special attention is given to Devnagari so that examination or investigation of ancient Indian literature can be done efficiently. The paper focus on the innovative methods for stepwise segmentation line segmentation and segmentation of character which are overlapping characters of Devnagri text. The steps followed to complete the process initially the character are segmented into lines, lines into words and then from each line the header lines of words are detected and then transformed as straight lines. Finally artificial neural network is used for classification.

Binary DE-Based Channel Selection and Weighted Ensemble of SVM Classification for Novel Brain–Computer Interface Using Devanagari Script-Based P300 Speller Paradigm

ABSTRACTP300 speller-based Brain–Computer Interface (BCI) is a direct communication from human brain to computer machine that is based on the decoding of brain responses generated by stimulation of a subject using P300 speller paradigm. This communication does not require any muscular movements. A novel Devanagari script (DS) input-based P300 speller system is proposed in this article. A novel 8 × 8 matrix consisting of Devanagari characters, digits, and special symbols has also been proposed as a DS paradigm. The character set associated with the DS paradigm is comparatively larger than the standard 6 × 6 English Row/Column (RC) paradigm. The problems related to crowding effect, fatigue, and task difficulty increase while using the DS paradigm for the P300 speller. This leads to either exhaustive or false detection of characters. In order to overcome these problems, a novel Weighted Ensemble of Support Vector Machines (WESVM)-based method has been proposed for classification. Further improvement in the system performance in terms of accuracy and reduced computational cost has been achieved by employing a binary Differential Evolution (DE) method for optimal channel selection. The proposed method has been tested on the EEG data collected from nine subjects using the DS paradigm. An average accuracy of 94.2% was achieved when the WESVM method was applied with the binary DE-based channel selection method.

A Review of Different Approaches Used for Devanagari Character Recognition

Handwriting Recognition System helps converting hand written text documents to digital form. Hand written recognition systems are different for different languages depending upon the script’s complexity. Extensive research have been performed on various languages across the globe including English but Devanagari Script has been left far behind due to its complex nature. This paper enlightens some of the popular research performed in recognizing Devanagari script. It also summarizes various advantage and scope of using different methodology including Bounding Box technique, Ostu’s algorithm, neural networks and many more.

Frequency Analysis Of Speech Signals For Devanagari Script And Numerals

This paper comprehends the analysis of frequency of spoken Devnagari script and Numerals from the primitive speech signals. Devnagari vowels and numerals play essential role in pronunciation of any word or counting. Particular vowel & number is dividing as starting, middle and end according to the time of occurrences in the word. The Devnagari script contains 12-vowels and 34-consonants and they are pre-owned in some Indian language like Hindi and 10 numerals (0-9) are worn in mathematics. Sound samples from multiple speakers were applied to obtain different features. Starting processing of data, that is normalizing and time-slicing was done using an amalgamation of Simulink and MATLAB. Later on, the same engines were used for calculation of fourier descriptions and correlations. The correlation permits comparison of the identical words or numeral uttered by the identical and different speakers. Hence the frequency has been calculated in statistical manner and produced a table among amplitude and frequencies. Standard deviation and mean can also be used in implementation of a voice-driven help setup at call centres of commercial organizations running in India and other foreign areas.

Handwritten Devanagari Characters and Numeral Recognition using Multi-Region Uniform Local Binary Pattern

Automated offline handwritten character recognition of Devanagari script is a growing area of research in the field of pattern recognition. A new approach for Devanagari handwritten character / digit recognition has been proposed in this paper. This approach employs Uniform Local Binary Pattern (ULBP) operator as the feature extraction method. This operator has great performance in research areas such as texture classification and object recognition, but it has not been used in Devanagari handwritten character/digit recognition problem. The proposed method extracts both local and global features. The proposed method have two steps, in the first step image is preprocessed to remove noise and to convert it to binary image and then resizing it to a fixed size of 48x48. In the second step, ULBP operator is applied to the image to extract global features then input image is divided into 9 blocks, ULBP operator is applied to each block to extract local features. Finally, global and local features are used to train Support Vector Machine(SVM). The proposed method has been tested on large set of handwritten character and numeral database and empirical results reveals that the proposed method yields very good accuracy (98.77%) . To establish the superiority of the proposed method, it has also been compared with the contemporary algorithms. The comparative analysis shows that the proposed method out performs the existing methods.

A Review on Various Techniques of Devanagari Script Recognition

A Review on Various Techniques of Devanagari Script Recognition

Extraction of Root Words using Morphological Analyzer for Devanagari Script

In India, more than 300 million people use Devanagari script for documentation. In Devanagari script, Marathi and Hindi are mainly used as primary language of Maharashtra state and national language of India respectively. As compared with English script, Devanagari script is reach of morphemes. Thus the lemmatization of Devanagari script is quite complex than that of English script. There is lack of resources for Devanagari script such as WordNet, ontology representation, parsing the keywords and their part of speech. Thus the overall task of information retrieval becomes complex and time consuming. Devanagari script document always carries suffixes which may cause problem in accurate information retrieval. We propose a method of extracting root words from Devanagari script document which can be used for information retrieval, text summarization, text categorization, ontology building etc. An attempt is made to design the Morphological Analyzer for Devanagari script. We have designed CORPUS containing more than 3000 possible stop words and suffixes for Marathi language. Morphological Analyzer can acts as a preliminary stage for developing any information retrieval application in Devanagari script. We have conducted the experiments on randomly selected Marathi documents and we found the accuracy of designed morphological analyzer is up to 96%.

A Comparative Analysis of Particle Swarm Optimization and Support Vector Machines for Devnagri Character Recognition: An Android Application

Devanagari script is widely used in the Indian subcontinent in several major languages such as Hindi, Sanskrit, Marathi and Nepali. Recognition of unconstrained (Handwritten) Devanagari character is more complex due to shape of constituent strokes. Hence character recognition (CR) has been an active area of research till now and it continues to be a challenging research topic due to its diverse applicable environment. As the size of the vocabulary increases, the complexity of algorithms also increases linearly due to the need for a larger search space. Devnagari script recognition systems using Zernike moments, fuzzy rule and quadratic classifier provide less accuracy and less efficiency. Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. In this paper techniques like particle swarm optimization and support vector machines are implemented and compared. An android phone is used for taking input character and MATLAB software for showing the recognized Devnagari character. For the connection between android device and MATLAB we are using PHP language. The particle swarm optimization technique provides accuracy up to 90%.

A Novel Weighted Edit Distance-Based Spelling Correction Approach for Improving the Reliability of Devanagari Script-Based P300 Speller System

P300 speller-based brain-computer interface is a direct communication from human-brain to computer machine without any muscular movements. In conventional P300 speller, a display paradigm is used to present alphanumeric characters to users and a classification system is used to detect the target character from the acquired electroencephalographic signals. In this paper, we present an $8\times 8$ matrix consisting of Devanagari characters, digits, and special characters as Devanagari script (DS)-based display paradigm. The larger size of the display paradigm as compared with conventional $6\times 6$ English row/column (RC) paradigm, involvement of matras and ardha-aksharas and similar looking characters in DS increase the adjacency problem, crowding effect, fatigue, and task difficulty. This results in deteriorated performance at the classification stage. Binary differential evolution algorithm was employed for optimal channel selection and support vector machine was used to classify target verses non target stimuli for the data set collected from ten healthy subjects using the DS-based paradigm. In order to further improve the system reliability in terms of higher accuracy at word prediction level, this paper proposes a novel spelling correction approach based on weighted edit distance (WED). A custom-built dictionary was incorporated and each misspelled word was replaced by a correct word of minimum WED from it. The proposed work is based on the validation of hypothesis that most of the target-error pairs lie in the same RC. Using the proposed spelling correction approach with optimal channel selection, an average accuracy of 99% was achieved at the word prediction level. The statistical analysis carried out in this paper shows that the proposed WED-based method improves the system reliability by significantly increasing in the accuracy of word prediction. This paper also validates that the proposed method performs better as compared to the conventional edit distance-based spelling correction approach.

A Hybrid Feature Extraction Algorithm for Devanagari Script

The efficiency of any character recognition technique is directly dependent on the accuracy of the generated feature set that could uniquely represent a character and hence correctly recognize it. This article proposes a hybrid approach combining the structural features of the character and a mathematical model of curve fitting to simulate the best features of a character. As a preprocessing step, skeletonization of the character is performed using an iterative thinning algorithm based on Raster scan of the character image. Then, a combination of structural features of the character like number of endpoints, loops, and intersection points is calculated. Further, the thinned character image is statistically zoned into partitions, and a quadratic curve-fitting model is applied on each partition forming a feature vector of the coefficients of the optimally fitted curve. This vector is combined with the spatial distribution of the foreground pixels for each zone and hence script-independent feature representation. The approach has been evaluated experimentally on Devanagari scripts. The algorithm achieves an average recognition accuracy of 93.4%.

Devanagari Handwritten Character Recognition using Hybrid Features Extraction and Feed Forward Neural Network Classifier (FFNN)

The process of recognizing scanned documents or machine printed documents using automated or semi-automated tools are resulted into wide range of applications in different real life domains. There are different techniques already introduced by various authors for efficient and accurate recognition of handwritten characters. As designing a method with 100 % accuracy of character recognition is challenging and unachievable task for researchers due to presence of noise, distinct styles of font under real time environment, therefore it is required to design recognition method by considering these characteristics of character recognition. This paper presenting online handwritten recognition framework by using efficient hybrid features codebook and Feed forward neural network (FFNN) to improve the recognition accuracy over Devanagari scripts. Along with the accuracy, another term which plays vital role of deciding the efficiency of recognition method is time required for recognition. Previous techniques giving the more accuracy for recognition, however feature extraction process takes longer time. Therefore such methods failed in real time applications. This paper majorly focusing on different recognition methods previously used and there recognition results, and then presenting our recognition method with its practical results for analysis. The results are varying by considering different image size in MATLAB.

Optimized Hindi Script Recognition using OCR Feature Extraction Technique

Feature Extraction in any handwritten script is very important part of OCR. If right feature extraction technique is used, then it will make the recognition of individual character easy. In this we will try to use some technique that extracts the feature of individual character. Devanagari script is the popular and widespread script of India which is mostly used in India. It is used to write Hindi, Nepali and Marathi. We will try to extract feature of Hindi language. Hindi Language consists of vowels, constants and various modifiers. Proper Feature Extraction technique on Hindi character is challenging task. In this we use simple Feature Extraction technique to recognize handwritten Devanagari script.

Structural Features Extraction for Devnagari and Bangla Language Documents

India is a multi-lingual, multi script country. Therefore developing a successful multi-lingual OCR, system for feature extraction of different scripts is a very important step. In this paper we discussed a Structural Features based algorithm for feature extraction. A family of procedures for measuring relevant shape information in a pattern in order to make the task of classifying pattern easy is called Feature Extraction. It analyses a segment of text and selects features which are unique to the text and can be used to identify it. The soul of pattern recognition system design is selection of features that are stable and representative. The real objective of feature extraction is to concentrate a situated of features, which expands the distinguishment rate with the slightest measure of components and to produce comparable feature set for mixture of cases of the same image. Feature extraction systems investigate the data archive picture and select a set of features that extraordinarily distinguishes and characterizes the character. Feature Extraction and Classification procedures are essential steps in character distinguishment methodology to attain to high distinguishment execution. Feature extraction is characterized as the issue of “separating from the crude information the data which is most important for characterization purposes, in the feeling of minimizing the inside class design variability while upgrading the between-class design variability”. In this paper we have developed structural features for Devnagari and Bangla script document.

Handwritten Devanagari Numeral Recognition by Fusion of Classifiers

Handwritten Devanagari Numeral Recognition by Fusion of Classifiers Recognition of handwritten Devanagari numerals has many applications especially in the field of postal automation, document processing and so on. Due to its vast applications, many researchers are actively working towards development of effective and efficient hand written character/numeral recognition. Devanagari script is widely used script in Indian sub-continent; also Devanagari script forms the basis for many other scripts in Indian sub-continent. In this paper, we have proposed a hybrid method to recognize handwritten Devanagari numerals. The proposed method uses, stacking approach to fuse the confidence scores from four different classifiers viz., Naive Bayes (NB), Instance Based Learner (IBK), Random Forest (RF), Sequential Minimal Optimization (SMO). Also, the proposed method extracts both local and global features from the handwritten numerals. In this work, we have used Fourier Descriptors as global shape feature. Whereas, the pixel density statistics from different zones of the numeral to describe the numerals locally. The proposed method has been tested on large set of handwritten numeral database and experimental results reveal that the proposed method yields the accuracy of 99.685%, which is the best accuracy reported so far for the datasets considered. Hence the proposed method outperforms contemporary algorithms.

Handwritten Devanagari Character Recognition System: A Review

India is a multi-lingual country consisting of eleven different scripts. Hindi is third most widely used language after English and Chinese. Hindi, the national language of India, is written in the Devanagari script. Devanagari script is also used for other languages such as Sanskrit, Marathi and Nepali. In India, Devanagari script is used by more than 500 million people for documentation. Devanagari script should be given special attention so that analysis of ancient Indian literature can be effectively done. Lot of work has been done in handwritten character recognition and lot of work is to be done. This paper presents a detailed analysis of research work related to devanagari character recognition. This paper presents comprehensive survey of handwritten character recognition system using different features of character and classifiers used.