A feature fusion based optical character recognition of Bangla characters using support vector machine

2017 ◽  
Author(s):  
Mst. Tasnim Pervin ◽  
Shyla Afroge ◽  
Aminul Huq
Keyword(s):  
Support Vector Machine ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Feature Fusion ◽  
Support Vector ◽  
Optical Character

scholarly journals Klasifikasi Analisis Sentimen Pada Gambar Meme Politik Dengan Library Tesseract Dan Algoritme Support vector machine

2019 ◽  
Vol 2 (1) ◽  
pp. 56-64
Author(s):  
Eko Sanjaya ◽  
Agi Prasetiadi ◽  
WAHYU ANDI SAPUTRA
Keyword(s):  
Support Vector Machine ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Support Vector ◽  
Optical Character ◽  
Non Linear

Meme merupakan penyebaran informasi dalam bentuk gambar. Berdasarkan data yang diperoleh, pengembangan meme mulai meningkat menjelang pemilu 2019. Informasi yang diperoleh dari meme politik beragam. Salah satunya memberikan dukungan untuk suatu partai atau tokoh politik atau digunakan untuk mengkritik / mencaci-maki partai politik atau tokoh. Sehingga diperlukan suatu sistem yang dapat mengklasifikasikan meme berdasarkan kelas Penelitian ini bertujuan untuk menciptakan sistem yang dapat mengklasifikasikan meme politik berdasarkan kelas. Algoritma yang akan digunakan dalam mengklasifikasikan adalah Support vector macine (SVM) dengan ekstraksi fitur TF-IDF. Library yang akan digunakan dalam optical character recognition (OCR) adalah Tesseract. Berdasarkan hasil pengujian diketahui bahwa akurasi yang dihasilkan oleh SVM linier lebih baik daripada SVM non-linear. Akurasi terbaik dalam SVM linear dengan kombinasi TF-IDF adalah 75.71%.

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 Optical Character Recognition of Amharic Documents

2007 ◽  
Vol 3 (2) ◽  
Author(s):  
Million Meshesha ◽  
C V Jawahar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Information Needs ◽  
Real Life ◽  
Principal Component ◽  
Support Vector ◽  
Extensive Literature ◽  
Large Set ◽  
Linear Discriminant ◽  
Optical Character

In Africa around 2,500 languages are spoken. Some of these languages have their own indigenous scripts. Accordingly, there is a bulk of printed documents available in libraries, information centers, museums and offices. Digitization of these documents enables to harness already available information technologies to local information needs and developments. This paper presents an Optical Character Recognition (OCR) system for converting digitized documents in local languages. An extensive literature survey reveals that this is the first attempt that report the challenges towards the recognition of indigenous African scripts and a possible solution for Amharic script. Research in the recognition of African indigenous scripts faces major challenges due to (i) the use of large number characters in the writing and (ii) existence of large set of visually similar characters. In this paper, we propose a novel feature extraction scheme using principal component and linear discriminant analysis, followed by a decision directed acyclic graph based support vector machine classifier. Recognition results are presented on real-life degraded documents such as books, magazines and newspapers to demonstrate the performance of the recognizer.

A Meetei Mayek Handwritten Digit Recognition System

2020 ◽  
Vol 17 (1) ◽  
pp. 334-339
Author(s):  
Chingakham Neeta Devi ◽  
Debaprasad Das ◽  
Haobam Mamata Devi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Speeded Up Robust Features ◽  
Bag Of Features ◽  
Optical Character ◽  
Handwritten Digit

Optical Character Recognition is an appealing field of work for research where an image containing text is given as input and text in the image is translated into an editable format. This paper proposes Meetei/Meitei Mayek Handwritten Digit Recognition System where an Isolated Handwritten Meetei Mayek Digit Database consisting of 10000 plus digits has been developed. This proposed Handwritten Meetei Mayek Digit Recognition System is an important component of Manipuri Meetei Mayek Optical Character Recognition system which is under development. For feature extraction, we have used State-of-Art techniques—Histogram of Oriented Gradients and Bag of Features Descriptor for Speeded Up Robust Features. Five classifiers have been employed for classification viz. Support Vector Machine, with Linear, Polynomial and Radial Basis Function kernels, K-Nearest Neighbours and Bootstrap Aggregating and compared in terms of accuracy. Support Vector Machine using Radial Basis Function Kernel has found to achieve the recognition accuracy with the highest value compared with the other classifiers with the extracted Histogram of Oriented Gradients and Bag of Features for Speeded Up Robust Features.

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