scholarly journals OPTICAL CHARACTER RECOGNITION MENGGUNAKAN ALGORITMA TEMPLATE MATCHING CORRELATION

2015 ◽  
Vol 5 (9) ◽  
Author(s):  
Suryo Hartanto ◽  
Aris Sugiharto ◽  
Sukmawati Nur Endah
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Optical Character

scholarly journals PENGENALAN WARNA BERBASIS ANDROID MENGGUNAKAN METODE TEMPLATE MATCHING

Compiler ◽  
2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Indra Hading Kurniawan ◽  
Nurcahyani Dewi Retnowati
Keyword(s):  
Success Rate ◽  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Matching Method ◽  
Color Recognition ◽  
Final Project ◽  
Optical Character ◽  
Simple Implementation ◽  
Image Object

Template matching method is a simple and widely used method to recognize patterns. The weakness of this algorithm is the limited model that will be used as a template as a comparison in the database such as shape, size, and orientation. The Extraction Feature algorithm addresses the problem of template models such as the shape, size, and orientation that exist in the matching template algorithm by mapping the characteristics of the image object to be recognized. Optical character recognition is used to translate characters into digital images into text formats. Its simple implementation makes the template matching method widely used. In this final project discusses the introduction of color in an image to be detected color, this color recognition is not fully successful because of the influence of lightness. The workings of this application take picture is by taking a picture and then the application identifies the color of any existing and will issue results in the form of text percent, with a success rate of 15% and 85% failure when detecting a color.

Effects of Ambient Light, Camcorder Settings, and Automated License Plate Reader Settings on Plate Transcription Rates

2002 ◽  
Vol 1804 (1) ◽  
pp. 56-61 ◽  
Author(s):  
Michael Plotnikov ◽  
Paul W. Shuldiner
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
License Plate ◽  
Ambient Light ◽  
Light Conditions ◽  
Video Images ◽  
Optical Character ◽  
Plate Reader

The ability of an automated license plate reading (ALPR) system to convert video images of license plates into computer records depends on many factors. Of these, two are readily controlled by the operator: the quality of the video images captured in the field and the internal settings of the ALPR used to transcribe these images. A third factor, the light conditions under which the license plate images are acquired, is less easily managed, especially when camcorders are used in the field under ambient light conditions. A set of experiments was conducted to test the effects of ambient light conditions, video camcorder adjustments, and internal ALPR settings on the percent of correct reads attained by a specific type of ALPR, one whose optical character recognition process is based on template matching. Images of rear license plates were collected under four ambient light conditions: overcast with no shadows, and full sunlight with the sun in front of the camcorder, behind the camcorder, and orthogonal to the line of sight. Three camcorder exposure settings were tested. Two of the settings made use of the camcorder’s internal light meter, and the third relied solely on operator judgment. The license plates read ranged from 41% to 72%, depending most strongly on ambient light conditions. In all cases, careful adjustment of the ALPR led to significantly improved read rates over those obtained by using the manufacturer’s recommended default settings. Exposure settings based on the operator’s judgment worked best in all instances.

scholarly journals Revolution by using Optical Character Recognition Technique to Identify Registered Number Plate

2019 ◽  
Vol 8 (10) ◽  
pp. 1185-1188
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Recognition Algorithm ◽  
Research Topic ◽  
Binary Mask ◽  
Segmentation Method ◽  
Optical Character ◽  
Small Parts ◽  
Recognition Technique

From past few years, the most interesting research topic is ANPR which registration of vehicles by their number plates. The purpose of this system is used for identifying number plate of numerous automobile. From automobile images, only number plate is extracted using binary mask method. And Optical Character Recognition (OCR) technique will be done with segmentation method. In segmentation, the numbers or characters on number plate are separated into small parts which is used to recognize using template matching in optical character recognition algorithm. As a result, the recognized number plate will be displayed. Also the result of this number plate is registered or not registered number plate will be displayed as a result.

scholarly journals Optical Character Recognition based on Template Matching

2019 ◽  
pp. 31-35
Author(s):  
Md. Anwar Hossain ◽  
Sadia Afrin
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Research Area ◽  
Text Document ◽  
Optical Character ◽  
Important Research Area ◽  
System Prototype ◽  
Text Images ◽  
Image Format

This paper presents an innovative design for Optical Character Recognition (OCR) from text images by using the Template Matching method.OCR is an important research area and one of the most successful applications of technology in the field of pattern recognition and artificial intelligence.OCR provides full alphanumeric visualization of printed and handwritten characters by scanning text images and converts it into a corresponding editable text document. The main objective of this system prototype is to develop a prototype for the OCR system and to implement The Template Matching algorithm for provoking the system prototype. In this paper, we took alphabet (A-Z and a-z), and numbers (0-1), grayscale images, bitmap image format were used and recognized the alphabet and numbers by comparing between two images. Besides, we checked accuracy for different fonts of alphabet and numbers. Here we used Matlab R 2018 a software for the proper implementation of the system.

Sum of Squared Difference (SSD) Template Matching Testing on Writing Learning Application

2020 ◽  
Vol 8 (4) ◽  
pp. 453
Author(s):  
Widya Dharma Sidi ◽  
I Gede Arta Wibawa
Keyword(s):  
Early Childhood ◽  
Feature Extraction ◽  
Mobile Learning ◽  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Matching Method ◽  
Writing Test ◽  
Optical Character

Abstract This research was conducted to determine the accuracy of the Sum of Squared Difference (SSD) Template Matching method in the Application of Learning Numbers Writing Games. This game application is an application created to help early childhood in recognizing Arabic numbers, namely numbers from 0 to 9. In the SSD Template Matching method there are several processes including Preprocessing, thinning, feature extraction, and classification (SSD template matching). In testing the game application involves 10 respondents who were asked to write numbers correctly as requested by the application. For each number writing test, it is tested by 3 times. From the tests conducted, obtained an accuracy of 94.67%. Keyword: Template Matching, Sum of Squared Difference (SSD), Education Game, Optical Character Recognition, Mobile Learning

scholarly journals KALKULATOR SAINTIFIK BERBASIS KAMERA

KONVERGENSI ◽  
2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Nenden Siti Fathonah ◽  
Achmad Yogie Pratama ◽  
Fajar Astuti Hermawati
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Optical Character

Sistem Optical Character Recognition (OCR) merupakan teknologi pengolahan citra untuk mengidentifikasi tulisan atau karakter yang terdapat pada sebuah gambar. Dalam menerjemahkan suatu citra, Optical Character Recognition melakukan segmentasi terlebih dahulu terhadap citra tersebut sehingga menjadi potongan – potongan gambar karakter. Setelah terbagi menjadi potongan – potongan gambar sistem OCR melakukan pengenalan pada masing – masing gambar karakter tersebut. Karakter dalam gambar yang di scan dengan OCR diubah menjadi text yang kemudian ditampilkan ke layar. Penelitian ini mengimplementasikan template matching dengan koefisien correlation untuk mengidentifikasi tulisan atau text yang terdapat pada sebuah citra. Dan kemudian hasil keluaran dari OCR akan diubah menjadi angka dan operator untuk selanjutnya dilakukan proses kalkulasi atau penghitungan. Dan hasil perhitungan kemudian ditampilkan ke layar. Dari beberapa percobaan diperoleh akurasi pengenalan dan perhitungan sebesar 85%.

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