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.

scholarly journals Product Label Reading System for Blind People using Support Vector Machine Algorithm

2019 ◽  
Vol 8 (6S2) ◽  
pp. 179-186
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Region Of Interest ◽  
Ground Truth ◽  
Support Vector ◽  
Svm Classifier ◽  
Stroke Width ◽  
Optical Character ◽  
Character Size ◽  
Reading System

Theoretical—This paper shows a camera based assistive content perusing of item marks from articles to support outwardly tested individuals. Camera fills in as fundamental wellspring of info. To recognize the items, the client will move the article before camera and this moving item will be identified by Background Subtraction (BGS) Method. Content district will be naturally confined as Region of Interest (ROI). Content is extricated from ROI by consolidating both guideline based and learning based technique. A tale standard based content limitation calculation is utilized by recognizing geometric highlights like pixel esteem, shading force, character size and so forth and furthermore highlights like Gradient size, slope width and stroke width are found out utilizing SVM classifier and a model is worked to separate content and non-content area. This framework is coordinated with OCR (Optical Character Recognition) to extricate content and the separated content is given as a voice yield to the client. The framework is assessed utilizing ICDAR-2011 dataset which comprise of 509 common scene pictures with ground truth.

scholarly journals Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code

2018 ◽  
Vol 14 (1) ◽  
pp. 19-25
Author(s):  
Taufik Fuadi Abidin ◽  
Abbas Adam AzZuhri ◽  
Fitri Arnia
Keyword(s):  
Support Vector Machines ◽  
Character Recognition ◽  
Classification Problem ◽  
The Other ◽  
Support Vector ◽  
License Plate ◽  
Chain Code ◽  
Area Code ◽  
Vector Machines ◽  
Multi Class Classification

A license plate is one of the vehicle identities. It consists of alphabetic characters and numbers and represents provincial and area code where the vehicle is registered. This article discusses the character recognition of plate number using zoning and Freeman Chain Code (FCC). Zoning divides character image into several zones i.e. 4, 6, and 8, and then, the pattern of each character in the zone is extracted using FCC as the numerical features. The character is then classified using Support Vector Machines (SVM). It is a multi-class classification problem with 36 categories. The results show that FCC features with 8 zones give the best accuracy (87%) when compared to the other two zones.

Sign in / Sign up

Export Citation Format

Share Document