Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Himakshi Choudhury"'
Publikováno v:
Pattern Recognition. 91:200-215
The representation of online handwriting is an important aspect of handwriting applications, which involves the extraction of various spatial and temporal attributes for analysis and individualization of handwritten patterns. In this work, a model ba
Publikováno v:
Expert Systems with Applications. 121:158-169
The segmentation of unconstrained handwriting is an important issue for both recognition and synthesis systems. In this direction, hidden Markov model (HMM) has been the most popular method for segmentation of continuous handwriting. It has been empl
Publikováno v:
Pattern Recognition Letters. 121:87-96
Handwriting is produced by the oscillatory motion of the hand in both horizontal and vertical directions, with a constant drift velocity along its writing direction. The velocity profiles of handwriting in these orthogonal directions have an invarian
Publikováno v:
ICDAR
Handwriting production is a complex mechanism of fine motor control, associated with mainly two degrees of freedom in the horizontal and vertical directions. The relation between the horizontal and vertical velocities depends on the trajectory shape
Publikováno v:
ICFHR
In this work, we consider recognizing online handwritten Assamese word (an Indic script) using the hybrid deep neural network - hidden Markov model (DNN-HMM) framework. The recognition task is generally challenging since Assamese handwriting is mixed
Publikováno v:
2016 IEEE Region 10 Conference (TENCON).
This paper presents a handwriting synthesis system developed using hidden semi-Markov model (HSMM) and studies the effect of its parameters namely, number of states and mixture components on the system. A systematic approach is proposed to optimize t
Publikováno v:
2016 IEEE Region 10 Conference (TENCON).
This paper describes an experimental study to compare the various steps involved in Hidden Markov Model (HMM) and Hidden Semi Markov Model (HSMM) approaches for recognition and synthesis, respectively. It presents different aspects of the two systems
Publikováno v:
2016 International Conference on Signal Processing and Communications (SPCOM).
A frequency count based two stage classification approach is proposed by combining generative and discriminative modeling principles for online handwritten character recognition. The first stage classifier based on Hidden Markov Model (HMM) returns t
Autor:
Himakshi Choudhury, Banriskhem K. Khonglah, H. L. Rufiner, Vineeth N Balasubramanian, S. R. M. Prasanna, Arghya Pal, Subhasis Mandal
Publikováno v:
2016 Twenty Second National Conference on Communication (NCC).
This work describes the development of online handwritten isolated Bengali numerals using Deep Autoencoder (DA) based on Multilayer perceptron (MLP) [1]. Autoencoders capture the class specific information and the deep version uses many hidden layers
Combining HMM and SVM based stroke classifiers for online Assamese handwritten character recognition
Autor:
Subhasis Mandal, Sanjeevan Devnath, Suresh Sundaram, S. R. Mahadeva Prasanna, Himakshi Choudhury
Publikováno v:
2015 Annual IEEE India Conference (INDICON).
Hidden Markov Models (HMMs) and Support Vector Machine (SVM) based classifiers are commonly used in the field of handwriting recognition. In this paper we investigate a technique of recognizing Assamese handwritten characters using HMMs and SVM strok