Devanagari

About: Devanagari is a research topic. Over the lifetime, 655 publications have been published within this topic receiving 7428 citations. The topic is also known as: Deva nagari & Hindi Script.

An Improved Approach for Devanagari Handwritten Characters Recognition System

Abstract: It is a very complicated task to recognize the handwritten characters and scanned data/images in recent years. The different sizes and writing methods of the characters play a critical role in clearly identifying the handwritten characters. This script's massive prevalence must be taken care of by using advanced technologies to connect to the real world to a greater depth. Machine Learning is one of the most popular technologies that has attracted the recent research work of handwritten character recognition using A.I. techniques. Various new technologies have been developed to execute fast neural networks with little exhaustive knowledge requirements. Here, we operate using Keras and Python libraries for building our model. The main aim of CNN is to recognize the training data and fit that training data into models that should help human beings. In this paper, an attempt has been made to construct and evaluate a simple individual learning algorithm (like k-means and SVM) using Keras to recognize isolated Devanagari handwritten characters datasets and assess the impact of variations in parameters in the learning phase. The proposed methodology gives a better result. The accuracy is better than individual algorithm performance.

HDWR_SmartNet: A Smart Handwritten Devanagari Word Recognition System Using Deep ResNet-Based on Scan Profile Method

Abstract: Handwritten script recognition is an important application in the machine learning domain and gaining more importance due to its numerous applications like automatic postal card sorting, digital signature verification, and processing of historical documents and also helps to develop applications for helping visually impaired people. English is the most widely spoken language that has witnessed much research work to read a script using the machine. Devanagari is also such a script that is used by a great number of people in the Indian Subcontinent. This chapter proposes Handwritten Devanagari Word Recognition (HDWR) using a method that is more analogous to human reading strategy. This chapter, contradictory to the traditional method, encourages a no-segmentation approach. Two novel approaches, Scan Profile and Sliding Window, have been introduced in this chapter to eliminate segmentation. First, the input word is passed to propose a no-segmentation approach where the window is defined using different methods. Secondly, each window is passed to the classifier for recognition purposes and the result is saved. Here no need to wait to segment the complete image. Immediately the window is recognized without knowing what is there in the window automatically by our designed state-of-the-art ResNet classifier like human reading. After one window recognition, the window moves next by the calculated stride to read the complete word. The proposed HDWR model successfully recognized Devanagari words with an accuracy of 86%.

Handwritten Devanagari word recognition using robust invariant feature transforms

Abstract: Local features in Images can be described by the various algorithms in Computer Vision. Handwritten Devanagari word scripts are usually present in different illumination, sizes, orientation and occlusion. These are recognized by finding the points of Interest followed by the extraction of features around these interest points. In this paper we discuss extraction of Invariant Features from Handwritten Devanagari words of various forms using the Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) techniques. On analysis of the patterns of word images and their recognition capability, the criteria for robust detection is derived. A dataset of over 1000 Devanagari words of various sizes and forms is created. The features extracted from the query image is subjected to Image matching by comparison to those features in the database using Random sample consensus. Comparison of Results indicate that SURF is computationally efficient while SIFT is more apt for detecting deformed images.

Handwritten Offline Devanagari Compound Character Recognition Using CNN

Abstract: AbstractCharacter recognition is the most challenging research topic due to its diverse applicable environment. Numerous research on Devanagari basic characters has been conducted, but due to difficulties associated, research on handwritten compound characters has received very little attention. The dilemma becomes much more complicated as a result of the different authors writing styles and moods. The traditional machine earning approach of character recognition focuses more on feature extraction, whereas the deep learning approach is a subset of machine learning that uses deep neural networks for learning. For current research work, we have created our own dataset for handwritten Devanagari compound characters. Our dataset has 5000 instances of 50 classes of compound characters collected from various writers of different age groups. This paper presents a convolutional neural network model for the recognition of Devanagari compound characters. We have implemented the ResNet model of CNN and used ReLu as an activation function as it effectively trains deep neural networks. We have implemented three-layer CNN, four-layer CNN, and five-layer CNN on our dataset, and its results are compared. We have achieved the highest accuracy of 100% on our dataset.KeywordsHandwritten character recognitionDevanagari compound charactersCNNResNetReLu

Automatic Recognition of Legal Amount Words of Bank Cheques in Devanagari Script: An Approach Based on Information Fusion at Feature and Decision Level

Abstract: Legal amount word recognition is an essential and challenging task in the domain of automatic Indian bank cheque processing Further intricacies get accumulated by inherent complexities in Devanagari script besides cursiveness present in handwriting Due to segmentation ambiguity and variability of constituent parts present in handwritten word analytical approach is inadequate, in contrast, to the holistic paradigm, where the word is taken indivisible entity Despite the proliferation of various feature representations, it still remains a challenge to get effective representation/description for holistic Devanagari words In this paper, we made an attempt to exploit robust, most discriminative and computationally inexpensive Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) for effective characterization of Devanagari legal amount words taking into account different writing styles and cursiveness Two models are proposed based on fusion strategies for word recognition In the first model, LBP and HOG features are fused at feature level and in second, fused at decision level In both models, recognition is performed by the nearest neighbour (NN) and support vector machine (SVM) classifiers For corroboration of the results, extensive experiments have been carried out on ICDAR 2011 Devanagari Legal amount word dataset Experimental results reveal that fusion based approaches are more robust than conventional approaches