Devanagari Text Research Articles

Handwritten Devanagari Text Recognition using Single Classifier Approach with VSPCA Scheme

In this research paper we used individual classifier approach for Handwritten Devanagari text recognition. We experimented different categorical classifiers namely Random Forest Classifier (RFC), Support Vector Machine (SVM), K Nearest Neighbor Classifier (KNN), Logistic Regression Classifier (LogRegr), Decision Tree Classifier (DTree). Seven different feature sets are used namely Eccentricity, Euler Number, Horizontal Histogram, Vertical Histogram, HOG Features, LBP Features, and Statistical Features. The experimentation is carried out on 9434 different characters whose features are extracted from 220 handwritten image documents from PHDIndic_11 dataset. We deduced and implemented a unique scheme namely VSPCA scheme. VSPCA is Vectorization, Scaling, and Principal Component Analysis carried out on all feature sets before being given for model training. We obtained varied accuracies using all these five classifiers on all these six feature sets in which 99.52% highest accuracy is observed.

Edge Detection Techniques Using Character Segmentation for OCR of Devanagari Text

An image processing technique called edge detection is used to find and identify the edges, curves, and objects in digital images. Edge detection includes various mathematical calculations that focus on locating the edges and curves where a digital image's brightness abruptly shifts or has discontinuities. In the fields of feature detection and feature extraction in image processing and computer vision, edge detection is a fundamental tool. The application of an edge detector results in the development of a set of connected curves that represent surface marks, character boundaries, and discontinuities in surface orientation. Character boundaries are described via edges. The majority of a feature is described by its edges. A group of pixels called an edge is used to describe an area where abrupt variations in intensity occur. Because it removes items of interest for actions like description, segmentation is a vital stage in an image recognition system. This paper focuses on edge detection techniques that can be implemented on characters for effective character recognition. Devanagari text includes curvy edges which need to be detected for proper recognition hence different edge detector operators are implemented to find the most effective amongst them to acquire clear edges of the text. There are several methods for segmenting images, including segmentation based on artificial neural networks, clustering, edges, regions, and thresholds. In this paper, edge-based segmentation is used. The Canny, Laplacian, Sobel, Prewitt, and Robert operators are used to detect edges, and the generated image is compared to the original binary image. The pre-processing phase includes the binarization step.

Feature extraction and classification techniques for handwritten Devanagari text recognition: a survey

Feature extraction and classification techniques for handwritten Devanagari text recognition: a survey

Segmentation of Lines and Words of Handwritten Devanagari Text using Connected Components with Statistics Method

The pre-processing activities for handwritten Devanagari text recognition includes an significant step called Segmentation. The segmentation accuracy of Devanagari text characters depends entirely on the accurately segmented lines and words in the handwritten documents. The process of segmenting lines and words correctly leads to many issues. More detailed information is lagging on the segmentation of lines and words from Devanagari text documents, whereas it is available more for other script documents in the literature. Here, we accomplished the task of segmenting the lines and words using Connected Components with Statistics Method on PHDIndic_11 dataset. Experimentation using above mentioned method resulted in line segmentation accuracy of 91.91% and word segmentation accuracy of 72.89% which outperforms over Global threshold and Otsu’s optimum threshold methods.

Character Segmentation and Skew Correction for Handwritten Devanagari Scripts: A Friends Technique

Optical Character Recognition (OCR) technology allows a computer to “read” text (both typed and handwritten) the way a human brain does.Significant research efforts have been put in the area of Optical Character Segmentation (OCR) of typewritten text in various languages, however very few efforts have been put on the segmentation and skew correction of handwritten text written in Devanagari which is a scripting language of Hindi. This paper aims a novel technique for segmentation and skew correction of hand written Devanagari text. It shows the accuracy of 91% and takes less than one second to segment a particular handwritten word.

Study of Different Features and Classification Techniques for Recognition of Handwritten Devanagari Text

Devanagari script is most popular and an older script in India. Millions of people all over the globe are using Devanagri script for various purposes such as communication, understanding the history, record keeping, research, etc. Recognition of handwritten Devanagari word is one of the popular area of research from decades because of its wide scope of applications. Different features and techniques of classification are the most important steps in the process of recognizing Devanagari handwritten word, are described in this paper.

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Implementation of a Novel Watermarking Technique for Devanagari Text

Implementation of a Novel Watermarking Technique for Devanagari Text

A Binarization Technique for Extraction of Devanagari Text from Camera Based Images

A Binarization Technique for Extraction of Devanagari Text from Camera Based Images

Handwritten Devanagari Script Recognition: A Survey

In India, many people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. Basically Character recognition techniques associate a symbolic identity with the image of a character. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with the program. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date.

Detection of Bold and Italic Character in Devanagari Script

a few works has been done for printed devanagari text in the area of optical character recognition. In this paper there is describing about a simple and fast algorithm for detection of italic and bold character in Devanagari script, without recognition of actual character. Here present an automatic information which tells us about the font type phase in the way of weight and slope. The process of identification and classification of italic and bold character can be used for making an accuracy of the text recognition system in the OCR. This simple and fast algorithm gives high accuracy and very easy to implement. KeywordsFont type phase, Binarization, Pixels, Noise

Offline Recognition of Devanagari Script: A Survey

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

Parallel Implementation of Devanagari Text Line and Word Segmentation Approach on GPU

Fast and accurate algorithms are necessary for Optical Character Recognition (OCR) systems to perform operations on document images such as pre-processing, segmentation, feature extraction, training and testing of classifiers and post processing. Text line and word segmentation are two important steps in any OCR system. Wrong segmentation may affect the accuracy rate of OCR systems. The segmentation is very challenging in cases of availability of different types of noises, degradations, and variation in writing and script characteristics. However, existing algorithms suffer from a flawed tradeoff between accuracy and speed. In this research work, Devanagri text line and word segmentation are carried out using modified standard profiling based segmentation approach and parallelized it on Graphics Processing Unit (GPU). The main goal of this research work is to make segmentation faster for processing a large number of document images using parallel implementation of algorithms on GPU. GPUs are emerging as powerful parallel systems at a cheaper cost. Our work employs extensive usage of highly multithreaded architecture and shared memory of multi-cored GPU. An efficient use of shared memory is required to optimize parallel reduction in Compute Unified Device Architecture (CUDA). Experimental results show that our method can achieve a speedup of about 20x-30x over the serial implementation when running on a GPU named GeForce 9500 GT having 32 cores.

RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT

Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17

Integrating knowledge sources in Devanagari text recognition system

The reading process has been widely studied and there is a general agreement among researchers that knowledge in different forms and at different levels plays a vital role. This is the underlying philosophy of the Devanagari document recognition system described in this work. The knowledge sources we use are mostly statistical in nature or in the form of a word dictionary tailored specifically for optical character recognition (OCR). We do not perform any reasoning on these. However, we explore their relative importance and role in the hierarchy. Some of the knowledge sources are acquired a priori by an automated training process while others are extracted from the text as it is processed. A complete Devanagari OCR system has been designed and tested with real-life printed documents of varying size and font. Most of the documents used were photocopies of the original. A performance of approximately 90% correct recognition is achieved.

Role of Context in Devanagari Script Recognition

Context plays a dominant role in final interpretation of Devanagari text. This is especially true for hand written texts. The upper and lower modifier symbols are rarely recognised correctly due to their incorrct placement, intermingling and/or incorrect segmentation. A Devanagari text recogniser has to associate these modifier symbols with the appropriate characters from the core strip to arrive at a “syntactically” meaningful word by making appropriate substitutions wherever required. This paper presents methodology and results for Devanagari text recognition using script composition contextual information for post-processing the recognised symbols.

Rule based contextual post-processing for devanagari text recognition

The spatial relationships among the constituent symbols of Devanagari script play an important role in the interpretation of Devanagari words. There are a number of constraints on these spatial relationships which characterise Devanagari script composition syntax. When the word composition is not found to be syntactically correct, the symbols are substituted with their resembling counterparts. The symbol substitution rules are mostly heuristic in nature. Human interpretation normally involves application of script composition syntax rules and the symbol substitution rules in an interleaved fashion. This paper presents a design of a post-processor which corrects the Devanagari symbol string based on this observation. The composition syntax checker is represented in the form of a finite state machine. The substitution rules are in the form of condition action pairs giving flexibility to the system for easy alteration. Each substitution rule has a penalty associated with it and the accumulated penalty value for a word gives a measure of its confidence level.

8. Brihaspati Sutra, or The science of politics according to the school of Brihaspati. Edited, with an introduction and English translation, by DrF. W. Thomas. … The Devanagari text prepared from his edition (in Roman script) … by Pt. Bhagavad Datta, B.A. The Punjab Sanskrit Series, No. I. 8¾×5¼, iii, 32, 31 pp. Lahore, 1921.

8. Brihaspati Sutra, or The science of politics according to the school of Brihaspati. Edited, with an introduction and English translation, by DrF. W. Thomas. … The Devanagari text prepared from his edition (in Roman script) … by Pt. Bhagavad Datta, B.A. The Punjab Sanskrit Series, No. I. 8¾×5¼, iii, 32, 31 pp. Lahore, 1921. - Volume 56 Issue 2