A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition

Chacko, Anitha Mary M. O.; Dhanya, P. M.

doi:10.1007/978-81-322-2208-8_2

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…To extract the pixel density features, a method called zoning [27] is employed. Here, the images are partitioned into zones of particular sizes that are predefined such that the features for each zone are measured.…”

Section: 2e Densitymentioning

confidence: 99%

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FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Mithra

Emmanuel

2018

Sādhanā

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Tuberculosis (TB) is one of the infectious diseases spread by the infectious agent Mycobacterium tuberculosis. Sputum smear microscopy is the primary tool used for the diagnosis of pulmonary TB, but has its limitations such as low sensitivity and large observation time. Hence, an automated technique is preferred for the diagnosis of TB. This paper develops a technique for TB diagnosis based on the bacilli count by proposing Fuzzy and Hyco-entropy-based Decision Tree (FHDT) classifier using sputum smear microscopic images. The proposed technique involves three steps: segmentation, feature extraction and classification. Initially, the input sputum smear microscopic image is subjected to a colour space transformation, for which a thresholding is applied to obtain the segmented result. Important features such as length, density, area and few histogram features are extracted for FHDT-based classification that classifies the segments into few-bacilli, non-bacilli and overlapping bacilli. An entropy function, called hyco-entropy, is designed for the optimal selection of feature. For further analysis of classification, that is, to count the number in the overlapping bacilli, the fuzzy classifier is adopted. FHDT classifier is evaluated in terms of Segmentation Accuracy (SA), Mean Squared Error (MSE) and Missing Count (MC) using microscopic images taken from ZNSM-iDB, where it can attain maximum mean SA of 0.954 and mean MC of 2.4.

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Section: 2e Densitymentioning

confidence: 99%

“…MSE refers to the average of the squares of values that deviate from the original and is derived from Eq. (27).…”

Section: Evaluation Metricsmentioning

confidence: 99%

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Mithra

Emmanuel

2018

Sādhanā

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“…Similarly, Raveena, et al [38] also suggested a theory related to structural elements (length of character in horizontal and vertical, number of endpoints, number of intersections in horizontal & vertical, number of loops, direction) for feature extraction and SVM for the classification of Malayalam characters. Chacko and Dhanya [39] applied zoning density, projection profile, chain code features, and HOG for feature extraction and compared the performance of all feature extraction methods, with ANN as the classifier. The accuracy achieved using zoning density, projection profile, chain code features, and HOG was 84.6%, 88.07%, 78.8%, and 94.23%, respectively, indicating that HOG recorded the highest accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Brahmi Word Recognition by Supervised Techniques

Gautam¹,

Chai²,

Afrin³

et al. 2020

Preprint

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Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

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“…The technology that will be developed by the research team to help the public in reading Batak script is a technology that applies computer vision and machine learning, namely character classification using the deep learning siamese neural network with one-shot learning [1]. The purpose of this research is to identify Batak script and produce Batak characters and sentences into Batak language using the siamese neural network (SINN) algorithm [2]- [5]. With this application, it is hoped that the community will be able to understand the Batak script so that the Batak script can still be preserved.…”

Section: Introductionmentioning

confidence: 99%

One-shot learning Batak Toba character recognition using siamese neural network

et al. 2023

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Siamese neural network (SINN) is an image processing model that compares the scores of two patterns. The SINN algorithm is a combination of the use of the double convolutional neural network (CNN) algorithm. By combined SINN with a one-shot learning algorithm, we can build an image model without requiring thousands of images for training. The test results from the SINN algorithm and one-shot learning show that this process was successful in matching the two data but was unable to produce labels from the data being tested. Because of this, the researcher decided to continue the implementation process using the CNN algorithm combined with single shot detection (SSD). By using a dataset of 5000, the recognition and translation of the Toba Batak script was successful. The percentage of average accuracy results from CNN and SSD in recognizing Toba Batak characters is 84.08% for single characters and 74.13% for mixed characters. While the percentage of average accuracy results for testing the breadth first search algorithm is 75.725%.

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A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition

Cited by 13 publications

References 12 publications

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Brahmi Word Recognition by Supervised Techniques

One-shot learning Batak Toba character recognition using siamese neural network

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