A Streamlined OCR System for Handwritten Marathi Text Document Classification and Recognition Using SVM-ACS Algorithm

Ramteke, Surendra P.; Gurjar, Ajay A.; Deshmukh, D. S.

doi:10.22266/ijies2018.0630.20

Cited by 12 publications

(6 citation statements)

References 19 publications

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“…SVM is a statistical learning system that is widely used for character identification. The SVM system has proven to be successful in many types of research due to its superiority in a wide range of applications such as image classification and retrieval, movement detection during vehicle traffic, and recognition of written texts [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Habeeb

Noman

Alkahtani

et al. 2021

Computational Intelligence and Neuroscience

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Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.

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Section: Methodsmentioning

confidence: 99%

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Habeeb

Noman

Alkahtani

et al. 2021

Computational Intelligence and Neuroscience

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“…Researchers usually judge the overall performance and efficiency of machine learning model using different performance metrics [26]. In our model we have implemented performance metrics so that we can understand if our model is performing well on our dataset.…”

Section: Performance Metricsmentioning

confidence: 99%

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

et al. 2021

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Parkinson's disease (PD) is a neurological illness that occurs by the degeneration of cells in the nervous system. Early symptoms include tremors or involuntary movements of the hands, arms, legs, and jaw. Currently, the only method to diagnose PD involves the observation of its prodromal symptoms. Moreover, detecting handwriting will work as a variable for clinitians to understand PD in patients better. With the advancement of technology, it is possible to build applications that will aid in diagnosing PD without any clinical intervention. The majority suffering from PD have handwriting abnormalities (referred to as micrographia), which is the most reported among earlier signs of the disease. So this research is undertaken by focusing on the implication of micrographia. For this purpose, handwritten images are collected from a group of 136 PD patients and 36 healthy patients. These images form a dataset of 800 images that are used to train a model which will accurately classify PD patients. To achieve this transfer learning is chosen because of its ability to produce accurate results regardless of the limited size of the dataset. Here, different models of transfer learning are trained to figure out the well-fitting model. It was observed that VGG-16 performed adequately with a training accuracy of 90.63% while a testing accuracy of 91.36%.

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“…Researchers generally evaluate the overall performance and also the efficiency of machine learning algorithms using these factors [26]. In our model we have evaluated performance metrics to understand how well our model is performing on given dataset.…”

Section: Performance Metricsmentioning

confidence: 99%

Human Gender Detection from Facial Images Using Convolution Neural Network

Sumi

Hossain

Islam

et al. 2021

Communications in Computer and Information Science

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Human gender detection which is a part of facial recognition has received extensive attention because of it's different kind of application. Previous research works on gender detection have been accomplished based on different static body feature for example face, eyebrow, hand-shape, body-shape, finger nail etc. In this research work, we have presented human gender classification using Convolution Neural Network (CNN) from human face images as CNN has been recognised as best algorithm in the field of image classification. To implement our system, at first a pre-processing technique has been applied on each image using image processing. The pre-processed image is passed through the Convolution, RELU and Pooling layer for feature extraction. A fully connected layer and a classifier is applied in the classification part of the image. To obtain a better result, we have implemented our system using different optimizers and also have used k fold cross-validation as deep learning approach. The whole method has been evaluated on two dataset collected from Kaggle website and Nottingham Scan Database. The experimented result shows a highest accuracy which is 97.44% using Kaggle dataset and 90% accuracy using Nottingham Scan Database.

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A Streamlined OCR System for Handwritten Marathi Text Document Classification and Recognition Using SVM-ACS Algorithm

Cited by 12 publications

References 19 publications

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

Human Gender Detection from Facial Images Using Convolution Neural Network

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