Amharic Handwritten Character Recognition Using Combined Features and Support Vector Machine

Reta, Betselot Yewulu; Rana, Dhara; Bhalerao, Gayatri Viral

doi:10.1109/icoei.2018.8553947

Cited by 15 publications

(3 citation statements)

References 14 publications

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“…It is worth mentioning the work done by Assabie et al [6] for handwritten Amharic word recognition. Betselot et al [23] also worked on handwritten Amharic character recognition. Both works used their own datasets and employed conventional machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Multi-script Handwritten Digit Recognition Using Multi-task Learning

Gondere,

Schmidt-Thieme,

Sharma

et al. 2021

Preprint

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Handwritten digit recognition is one of the extensively studied area in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourage the development of robust and multipurpose systems. Additionally working on multi-script digit recognition enables multi-task learning, considering the script classification as a related task for instance. It is evident that multi-task learning improves model performance through inductive transfer using the information contained in related tasks. Therefore, in this study multi-script handwritten digit recognition using multi-task learning will be investigated. As a specific case of demonstrating the solution to the problem, Amharic handwritten character recognition will also be experimented. The handwritten digits of three scripts including Latin, Arabic and Kannada are studied to show that multi-task models with reformulation of the individual tasks have shown promising results. In this study a novel way of using the individual tasks predictions was proposed to help classification performance and regularize the different loss for the purpose of the main task. This finding has outperformed the baseline and the conventional multi-task learning models. More importantly, it avoided the need for weighting the different losses of the tasks, which is one of the challenges in multi-task learning.

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Section: Related Workmentioning

confidence: 99%

Multi-script Handwritten Digit Recognition Using Multi-task Learning

Gondere,

Schmidt-Thieme,

Sharma

et al. 2021

Preprint

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“…K-nearest neighbor classifier results in an accuracy of 80.34% for HOG and 76.42% for zoning-based density features using 10-fold cross validation. An attempt is made by Reta et al [7] to address the challenges and difficulties of Amharic handwritten character recognition. They combined various feature extraction techniques such as HOG, local binary pattern (LBP) and geometrical features.…”

Section: Introductionmentioning

confidence: 99%

Recognition of compound characters in Kannada language

Narasimhaiah

Rangarajan

2022

IJECE

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<p><span lang="EN-US">Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes.</span></p>

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“…Research on Amharic character recognition has been carried out using a combination of features and Support Vector Machine. The paper discusses the combination of various feature extraction techniques and SVM for the introduction of Amhari characters [10]. Related research on character recognition has been carried out on Arabic characters using decision trees and perception codes.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Balinese Script Recognition on Palm Leaf Manuscript using Projection Profile and K-Nearest Neighbor

Sutramiani

Darma

Arsa

2022

JIM

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This paper presents a simple approach to the handwritten Balinese script characters recognition in palm-leaf lontar manuscripts. The Lontar manuscript is one of the cultural heritages found in Bali. Lontar manuscripts are written using a pengrupak, which is a kind of knife for writing on palm leaves. To give color to the results of the writing, candlenut is used so that the writing appears clear. In this paper, we apply the projection profile at the segmentation stage to get the handwritten Balinese script characters in the lontar manuscript. The palm leaf manuscript that we use is the Wariga Palalubangan palm leaf. The recognition process is carried out by implementing K-Nearest Neighbor in the recognition process. The recognition was made on the Wianjana script obtained from lontar manuscripts using 720 images consisting of 18 classes as dataset training. The test results showed that the level of recognition accuracy was obtained by 52% in the characters of handwritten Balinese scripts derived from lontar manuscripts and 92% in the characters of handwritten Balinese scripts on paper.

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Amharic Handwritten Character Recognition Using Combined Features and Support Vector Machine

Cited by 15 publications

References 14 publications

Multi-script Handwritten Digit Recognition Using Multi-task Learning

Multi-script Handwritten Digit Recognition Using Multi-task Learning

Recognition of compound characters in Kannada language

Handwritten Balinese Script Recognition on Palm Leaf Manuscript using Projection Profile and K-Nearest Neighbor

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