Devanagari Character Recognition Research Articles

HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters

<p>Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.</p>

Machine Learning Algorithms for Handwritten Devanagari Character Recognition: A Systematic Review

Machine Learning Algorithms for Handwritten Devanagari Character Recognition: A Systematic Review

Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Devanagari character and digit recognition are a difficult undertaking because writing style depends on a person’s traits and differs from person to person. We get more precise results in digit recognition, thanks to deep learning convolutional neural networks (CNNs), which function similarly to the human brain. In this study, the CNN method was put into practice and contrasted with the feed-forward neural network and random forest approaches. In comparison to previous methods, CNN has reportedly provided an accuracy rating of up to 99.2%. CNN is effective with both organized and unstructured data, including pictures, video, and audio.

Devanagari Character Recognition Using Deep Learning

The Devanagari script is one of the broadly used scripts of India and is advanced from the Brahmi script. It includes forty-seven number one alphabets, 14 vowels, 33 consonants, and 10 digits. There isn't any capitalization of letters, like in the English language. The Devanagari script includes consonants and modifiers. Deep gaining knowledge of strategies is carried out to extract capabilities and apprehend the characters in an image. A Deep Convolutional Neural Network (DCNN) had been included to extract capabilities and classify the entered images. Consecutive convolutional layers are used in this manner which brings introduced gain withinside the manner of extracting higher-degree capabilities. The output of the CNN layers is fed to the completely related layers. The version has applied the use of Keras libraries on the pinnacle of a TensorFlow backend. Finally, the threat or possibility rating of every person is decided and the person with the very best possibility rating is proven because of the output. Handwritten Devanagari Character Recognition is extra tough in contrast to the popularity of the Roman characters.

Handwritten Character Recognition using Deep Learning and Neural Network

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in different offices, educational institutes, healthcare units, commercial units and banks etc., where the documents that are handwritten are dealt more frequently. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, a recognition system based on neural network has been proposed for Devanagari (Marathi) alphabets. Each of the characters that are extracted through query image is resized and is then passed to the neural networks for the process of recognition.

SVM-PCA Based Handwritten Devanagari Digit Character Recognition

Objective: The blended fusion of Support Vector Machine (SVM) and Principal Component Analysis (PCA) have been widely used in recognizing handwritten digit characters of Devanagari script. The feature information from the character is extracted using its skeleton structure which optimally reduce data dimensionality using PCA. There is ample information available on handwritten charac-ter recognition on Indian and Non-Indian scripts but very few article emphasized on recognition of Devanagari scripts. Therefore, this paper presents an efficient handwritten Devanagari character recognition system based on block based feature extraction and PCA-SVM classifier. Methods: We have collected samples of handwritten Devanagari characters from different handwritten experts for classification. Results: For experimental work, total of 100 images having Devanagari digit characters been used for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 96.6 % and 96.5% for 5 & 10 fold validations with 70% training and 30% testing data using block based feature and SVM classifier having different kernels. Conclusion: The obtained results achieve maximum accuracy using SVM classifier for digit character recognition. In future deep learning networks will be considered for accuracy enhancement and precision.

Review for Handwritten Devanagari Character Recognition using ML Algorithms

Devanagari Character Recognition is a system in which handwritten Image is recognized and converted into a digital form. Devanagari handwritten character recognition system is based on Deep learning technique, which manages the recognition of Devanagari script particularly Hindi. This recognition system mainly has five stages i.e. Pre-processing, Segmentation, Feature Extraction, Prediction and Post processing. This paper has analyzed the approach for recognition of handwritten Devanagari characters. There are various approaches to solve this. Some of the methods along with their accuracy and techniques used are discussed here. Depending upon the dataset and accuracies of each character the techniques differs.

A scene perception system for visually impaired based on object detection and classification using CNN

In this paper we have developed a system for visually impaired people using OCR and machine learning. Optical Character Recognition is an automated data entry tool. To convert handwritten, typed or printed text into data that can be edited on a computer, OCR software is used. The paper documents are scanned on simple systems with an image scanner. Then, the OCR program looks at the image and compares letter shapes to stored letter images. OCR in English has evolved over the course of half a century to a point that we have established application that can seamlessly recognize English text. This may not be the case for Indian languages, as they are much more complex in structure and computation compared to English. Therefore, creating an OCR that can execute Indian languages as suitably as it does for English becomes a must. Devanagari is one of the Indian languages spoken by more than 70% of people in Maharashtra, so some attention should be given to studying ancient scripts and literature. The main goal is to develop a Devanagari character recognition system that can be implemented in the Devanagari script to recognize different characters, as well as some words.

Review Paper Devanagari and Gurumukhi Character Recognition

Review Paper Devanagari and Gurumukhi Character Recognition

Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index) ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

Ensembling: Model of histogram of oriented gradient based handwritten devanagari character recognition system

Ensembling: Model of histogram of oriented gradient based handwritten devanagari character recognition system

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.

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.

Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction

Handwritten Devanagari off-line characters recognition is a challenging task due to peculiarities involved in the writing styles of different person. We are collected features from 64 dimensional feature extraction techniques and shadow features for the Devanagari character recognition in this proposed scheme. These calculated features are used for further classification. Features which are used here for recognition means histograms of direction chain code of the contour points of the characters [4]. The features are classified using neural network and selected the best result by weighted majority voting technique.

Allograph modeling for online handwritten characters in devanagari using constrained stroke clustering

Writer-specific character writing variations such as those of stroke order and stroke number are an important source of variability in the input when handwriting is captured “online” via a stylus and a challenge for robust online recognition of handwritten characters and words. It has been shown by several studies that explicit modeling of character allographs is important for achieving high recognition accuracies in a writer-independent recognition system. While previous approaches have relied on unsupervised clustering at the character or stroke level to find the allographs of a character, in this article we propose the use of constrained clustering using automatically derived domain constraints to find a minimal set of stroke clusters. The allographs identified have been applied to Devanagari character recognition using Hidden Markov Models and Nearest Neighbor classifiers, and the results indicate substantial improvement in recognition accuracy and/or reduction in memory and computation time when compared to alternate modeling techniques.

Handwritten Devanagari Character Recognition using Neural Network

In this digital era, most important thing is to deal with digital documents, organizations using handwritten documents for storing their information can use handwritten character recognition to convert this information into digital. Handwritten Devanagari characters are more difficult for recognition due to presence of header line, conjunct characters and similarity in shapes of multiple characters. This paper deals with development of grid based method which is combination of image centroid zone and zone centroid zone of individual character or numerical image. In feature extraction using grid or zone based approach individual character or numerical image is divided into n equal sized grids or zones then average distance of all pixels with respect to image centroid or grid centroid is computed. In combination of image centroid and zone centroid approach it computes average distance of all pixels present in each grid with respect to image centroid as well as zone centroid which gives feature vector of size 2xn features. This feature vector is presented to feed forward neural network for recognition. Complete process of Devanagari character recognition works in stages as document preprocessing, segmentation, feature extraction using grid based approach followed by recognition using feed forward neural network.

Devnagari Handwriting Recognition using STANN

approaches to recognize the handwritten characters and numerals such as online cursive handwriting and numerals recognition have been proposed. Most of them are based on neural network approaches. In this paper, a technique for continuous recognition of handwritten Devanagari characters is proposed. This approach employs a method based on Spatio-Temporal Artificial Neural Network (STANN). The proposed method is efficient in the field of online handwriting recognition because of the property of STANN to detection of spikes of the continuous input signals. The method is based on different steps of recognition. The results of signals are extracted from the input handwritten characters where the spikes signals are generated by the continuous signals of the input character. A new algorithm using STANN is proposed with results in this paper.

Devanagari Character Recognition: A Short Review

Optical character recognition is a vital task in the field of pattern recognition. English character recognition has been extensively studied by many researchers but in case of Indian languages which are complicated; the research work is very limited. Devanagari is an indian script used by huge number of indian people. Devanagari forms the basis for several indian languages including Hindi, Sanskrit, Kashmiri, Marathi and so on. This article presents a review of earlier research work related to devanagari character recognition along with some applications of optical character recognition system.

A Novel Approach of Handwritten Devanagari Character Recognition through Feed Forward Back Propagation Neural Network

Handwritten character recognition plays an important role in the modern world. It can solve more complex problems and makes human’s job easier. The present paper portrays a novel approach in recognizing handwritten devanagari character through feed forward back propagation neural network. All the experiments are conducted by using the Artificial Neural Network tool of Matlab.

Handwritten Devanagari Lipi using Support Vector Machine

The handwritten recognition is a one of the basic biometric recognition technique. Different technique and features are used for the faithful recognition characters. In this paper we have proposed a SVM (support vector machine) based technique for Devanagari character recognition. The Devanagari characters have very correlation to each other. This feature of the Devanagari lipi make difficult to faithful recognition. The energy features of segment characters are used for the classification. The more no. of segmentation improves the recognition rate. The different recognition rates with no. of segment are used in this paper. The recognition rate is also developed on the kernel of SVM. The result of different kernel is also given in this paper.