Handwritten 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

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.

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

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.

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.

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.

Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition

In this paper, a system based Handwritten Devanagari Character Recognition (HDCR) is proposed. The paper presents an experimental assessment of the efficiency of various methods based on Invariant Moments for handwritten devanagari vowels recognition. The technique is independent of size, slant, orientation, translation and other variations in handwritten vowels. For segmentation of the devanagari words, the header line (Shirorekha) , plays vital role. The same tool with vertical and horizontal projection has been adapted to isolate the 13 vowels in five different groups. In order to enhance the performance of the system, an attempt has been made to compute invariant moments by small perturbation in image and information is extracted from the perturbation. But it was found that, another local feature descriptor, image partition in different zoning is better representation of the features than perturbation. The other method of image partition with different ways found better. 10 samples of each vowel from 25 people have been sampled and a database was prepared. Individual image is normalized to 40X40 pixel size. The Fuzzy Gaussian Membership function has been adopted for classification. The success rate of the method is found to be 94.56.