scholarly journals Pengenalan Karakter Tulisan Tangan Menggunakan Ekstraksi Fitur Bentuk Berbasis Chain Code

2020 ◽  
Vol 7 (4) ◽  
pp. 683
Author(s):  
Saniyatul Mawaddah ◽  
Nanik Suciati
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Character Recognition ◽  
Handwriting Recognition ◽  
Support Vector ◽  
Shape Feature ◽  
Performance Study ◽  
Chain Code ◽  
Chain Codes ◽  
Shape Feature Extraction

<p class="Abstrak">Pengenalan karakter tulisan tangan pada citra merupakan suatu permasalahan yang sulit untuk dipecahkan, dikarenakan terdapat perbedaan gaya penulisan pada setiap orang. Tahapan proses dalam pengenalan tulisan tangan diantaranya adalah <em>preprocessing</em>, ekstraksi fitur, dan klasifikasi. <em>Preprocessing</em> dilakukan untuk merubah citra tulisan tangan menjadi citra biner yang hanya mempunyai ketebalan 1 pixel melalui proses binerisasi dan <em>thining</em>. Kemudian pada tahap ekstraksi fitur, dipilih fitur bentuk karena fitur bentuk memiliki peran yang lebih penting dibanding 2 fitur visual lainnya (warna dan tekstur) pada pengenalan karakter tulisan tangan. Metode ekstraksi fitur bentuk yang dipilih dalam penelitian ini adalah metode berbasis <em>chain code</em> karena metode tersebut sering digunakan dalam beberapa penelitian pengenalan tulisan tangan. Pada penelitian ini, dilakukan studi kinerja dari ekstraksi fitur berbasis <em>chain code</em> pada pengenalan karakter tulisan tangan untuk mengetahui metode terbaiknya. Tiga metode ekstraksi fitur berbasis <em>chain code</em> yang digunakan dalam penelitian ini adalah <em>freeman chain code</em>, <em>differential chain code</em> dan <em>vertex chain code</em>. Setiap citra karakter diekstrak menggunakan 3 metode tersebut dengan tiga cara yaitu ekstraksi secara global, lokal 3x3, 5x5, dan 7x7. Setelah esktraksi fitur, dilakukan proses klasifikasi menggunakan support vector machine (SVM). Hasil eksperimen menunjukkan akurasi terbaik adalah pada model citra 7x7 dengan nilai akurasi <em>freeman chain code</em> sebesar 99.75%, <em>differential chain code</em> sebesar 99.75%, dan <em>vertex chain code</em> sebesar 98.6%.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The recognition of handwriting characters images is a difficult problems to be solved, because everyone has a different writing style. The step of handwriting recognition process are preprocessing, feature extraction, and classification. Preprocessing is done to convert handwritten images into binary images that only have 1 pixel thickness by using binarization and thinning. Then, in the feature extraction we select shape feature because it is more important than two other visual features (color and texture) in handwriting character recognition. Shape feature extraction method chosen in this research is chain code method because this method is often used in several studies for handwriting recognition. In this study, a performance study of feature extraction based on chain codes was carried out on handwriting character recognition to know the best chain code method. The three shape feature extraction based on chain code used in this study are freeman, differential and vertex chain codes. Each character image is extracted using these 3 methods in three ways: extraction globally, local 3x3, 5x5, and 7x7. After the extraction feature, the classification process is carried out using the support vector machine (SVM). The experimental results show that the best accuracy is in the 7x7 image model with the value of freeman chain code accuracy of 99.75%, the differential chain code of 99.75%, and the vertex chain code of 98.6%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Support Vector Machine (SVM) in Handwritten Character Recognition Using Freeman Chain Code (FCC)

2014 ◽  
Vol 20 (10) ◽  
pp. 2171-2175 ◽  
Author(s):  
Dewi Nasien ◽  
Habibollah Haron ◽  
Aini Najwa Azmi ◽  
Siti Sophiayati Yuhaniz
Keyword(s):  
Support Vector Machine ◽  
Character Recognition ◽  
Support Vector ◽  
Handwritten Character Recognition ◽  
Chain Code ◽  
Handwritten Character

scholarly journals IMPLEMENTASI ZONING DAN FITUR ARAH SEBAGAI EKSTRAKSI FITUR PADA PENGENALAN TULISAN TANGAN AKSARA BALI

2021 ◽  
Vol 4 (1) ◽  
pp. 85-92
Author(s):  
I Komang Arya Ganda Wiguna ◽  
I Made Dwi Putra Asana
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Template Matching ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Optimal Method ◽  
Chain Code ◽  
Feature Template ◽  
Degree Of Similarity ◽  
Test Result

Character recognition is one of the most researched fields in computer science. Combining the field of digital image processing and pattern recognition is a challenge in determining the most optimal method combination to complete character recognition. Balinese script is one of the regional scripts used in Balinese literary. The challenge with Balinese script is that some of its characters have a degree of similarity. So far, several methods of feature extraction that have been studied for Balinese script are modified direction feature, template matching, image centroid zone and zone centroid zone, local binary pattern. In this research, we combine methods based on zoning and directional features. The methods used are ICZ, ZCZ and freeman chain code to find the characteristics of Balinese script handwriting. The addition of chain code method aims to determine the value around the foreground point. The results of feature extraction will be used as input in the Support Vector Machine for the classification process. The test result shows that the combination of the ICZ, ZCZ and freeman chain code methods produces an accuracy of 89.09%, while the combination of ICZ and ZCZ produces 88.06% of accuracy. The SVM kernels compared use linear kernels.

scholarly journals Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

2021 ◽  
Vol 9 (3) ◽  
pp. 150-156
Author(s):  
Hanimatim Mu'jizah ◽  
Dian Candra Rini Novitasari
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Image Data ◽  
Extraction Methods ◽  
Support Vector ◽  
Shape Feature ◽  
Histogram Of Oriented Gradient ◽  
Mammogram Image ◽  
Aided Diagnosis ◽  
Shape Feature Extraction

Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

scholarly journals PERANCANGAN DAN IMPLEMENTASI DIRECTIONAL FEATURE EXTRACTION DAN SUPPORT VECTOR MACHINES UNTUK MENERJEMAHKAN KATA DENGAN PENGENALAN HURUF HIRAGANA DALAM BAHASA JEPANG KE BAHASA INDONESIA BERBASIS ANDROID

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fardilla Zardi Putri ◽  
Budhi Irawan ◽  
Umar Ali Ahmad
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Support Vector ◽  
Optical Character ◽  
Vector Machines ◽  
Bahasa Indonesia

Pada era global ini menguasai bahasa selain bahasa Indonesia merupakan salah satu kebutuhan penting yang harus dimiliki setiap orang. Banyak orang berkunjung ke negara lain untuk melakukan banyak kegiatan seperti bekerja, belajar, bahkan berlibur. Salah satu negara yang banyak dikunjungi adalah negara Jepang. Negara Jepang memiliki bentuk huruf yang berbeda dengan huruf latin pada umumnya. Untuk mempelajari bahasa Jepang tersebut dibutuhkan pemahaman dengan huruf-hurufnya. Seiring dengan berkembangnya teknologi, pengenalan karakter atau sering Optical Character Recognition (OCR) merupakan salah satu aplikasi teknologi pada bidang pengenalan karakter atau pola dan kecerdasan buatan sebagai mesin pembaca. Pada penelitian ini, akan dirancang sebuah aplikasi penerjemah kata dalam bahasa Jepang berbasis Android dengan memanfaatkan prinsip dasar OCR dengan menggunakan metode Directional Feature Extraction dan Support Vector Machine. Pengujian yang dilakukan memberikan hasil terbaik pada nilai akurasi yang dicapai dengan menggunakan metode Directional Feature Extraction dan Support Vector Machine adalah 85,71%. Pada penelitian ini, menggunakan 104 data latih. Hasil pengujian Beta atas empat poin, yaitu tampilan aplikasi, waktu respons sistem, ketepatan penerjemahan, dan manfaat aplikasi menunjukkan aplikasi dapat diklasifikasikan baik.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

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