dc.contributor.author |
Doshi, Priyank D. |
|
dc.contributor.author |
Vanjara, Pratik |
|
dc.date.accessioned |
2023-05-16T04:16:42Z |
|
dc.date.available |
2023-05-16T04:16:42Z |
|
dc.date.issued |
2022-01 |
|
dc.identifier.citation |
Doshi,P.&Vanjara,P.(2022).Hybrid machine learning in classification methods for HCR in gujarati language.International Multidisciplinary journal of applied research,1(6),57-61. |
en_US |
dc.identifier.issn |
2321-7073 |
|
dc.identifier.uri |
http://10.9.150.37:8080/dspace//handle/atmiyauni/960 |
|
dc.description.abstract |
The problem of recognizing Gujarati Handwritten character with vowels opening new future
scope where one can use smart phone, website or any handy scanner to convert hand written
Gujarati Language into text. It will be very effective to give education in mother language at
primary level. Public, Private and Government sectors will be benefited when they get any
hand written Guajarati Script and they can directly convert it into softcopy or into text form.
There are many methods used to solve this problem.Using CNN we can improve new
algorithm depending on training data set, mathematical model and other intricacy.
Convolutional Neural Network or machine learning is very useful for this. Still there are more
chances for improvement and rising accuracy using Machine learning in combination of Deep
Learning as a hybrid model. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
International Multidisciplinary journal of applied research |
en_US |
dc.subject |
machine learning |
en_US |
dc.subject |
deep learning |
en_US |
dc.subject |
hand written character recognition (hcr) |
en_US |
dc.subject |
support vector machine |
en_US |
dc.subject |
artificial neural network (ann) |
en_US |
dc.subject |
convolutional neural network |
en_US |
dc.title |
Hybrid machine learning in classification methods for HCR in gujarati language |
en_US |
dc.type |
Article |
en_US |