Offline MODI script character recognition using deep learning techniques

Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning and making artificial intelligence and human-computer interaction spread with big strides. India is a heritage country where traditions, religions and language...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2023-06, Vol.82 (14), p.21045-21056
Main Authors: Chandankhede, Chaitali, Sachdeo, Rajneeshkaur
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Deep learning
Handwriting recognition
Languages
Literature reviews
Machine learning
Multilayers
Multimedia Information Systems
Neural networks
Optical character recognition
Performance evaluation
Special Purpose and Application-Based Systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning and making artificial intelligence and human-computer interaction spread with big strides. India is a heritage country where traditions, religions and languages are quite varied. MODI script is one of the oldest written forms of media. Most of the early written knowledge on subjects like medicine, Buddhist ideology, food habits and horoscope has been written using MODI script. MODI is one of the languages that present special challenge to OCR. The main challenge in MODI script is that it is mostly cursive and few characters look similar. The deep learning methods like InceptionV3 and RestNet on Modi script seems not experimented yet as per literature review. This motivates to apply the deep learning methods to offline handwritten character recognition using Residual and InceptionV3 framework. The handwritten Modi barakhadi dataset is prepared by collecting samples from around 25 different people. The dataset of size 7721 is experimented. The individual characters are cut and pre-processed using Otsu binarization technique. The performance evaluation of pre-processed data is done using both algorithms on the real-world handwritten character database written by different people. Processed images recognized through RestNet50 gives testing accuracy of 94.552% with precision of the model 0.86. Processed images recognized through InceptionV3 gives testing accuracy of 93.923% with precision of the model 0.843.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-14476-0