scholarly journals Program Konversi Citra Notasi Balok Menjadi Notasi Angka

2018 ◽  
Vol 2 (2) ◽  
pp. 119
Author(s):  
Hendy Gunawan ◽  
Janson Hendryli ◽  
Dyah Erny Herwindiati
Keyword(s):  
Success Rate ◽  
Character Recognition ◽  
Template Matching ◽  
Recognition System ◽  
Input Image ◽  
Character Segmentation ◽  
Music Notation ◽  
Image Conversion ◽  
Template Image ◽  
Line Segmentation

The Image Conversion Program of Music Notation being Numeric Notation is a character recognition system that accepts input in form of music notation image that produces an output of a DOCX file containing the numeric notation from the input image. Music notation has notation value, ritmic value and written with a music stave. The system consists of four main processes: preprocessing (grayscale and thresholding), notation line segmentation, notation character segmentation, and template matching. Template matching is used to recognize the music notation that obtained after segmentation. The recognition process obtained by comparing the image with the template image that has been inputted before to the database. This system has 100% success rate on segmentation of the character and success rate 38,4843% on the character recognition with template matching.

scholarly journals Pengenalan Karakter Plat Nomor Mobil Secara Real Time

2013 ◽  
Vol 7 (1) ◽  
pp. 35
Author(s):  
Ikhwan Ruslianto ◽  
Agus Harjoko
Keyword(s):  
Success Rate ◽  
Real Time ◽  
Character Recognition ◽  
Template Matching ◽  
Component Analysis ◽  
Recognition System ◽  
Character Segmentation ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Complex Background

AbstrakPengenalan plat nomor di Indonesia biasanya digunakan pada sistem parkir yang masih dilakukan secara manual, yaitu dengan mencatat karakter plat nomor oleh petugas jaga parkir. Padahal pengenalan plat nomor tidak hanya dilakukan untuk system perparkiran tetapi dapat digunakan untuk menemukan kendaraan yang melanggar peraturan lalu lintas dijalan raya secara real time, misalnya pelaku tabrak lari pada kecelakaan maupun kendaraan yang melanggar rambu-rambu lalu lintas.Penelitian ini memberikan alternatif pengenalan karakter plat nomor mobil menggunakan metode connected component analysis dan matching sehingga dapat menyelesaikan permasalahan dengan background yang kompleks dan mobil yang bergerak dijalan raya.Metode connected component analysis berhasil melakukan proses segmentasi plat dan segmentasi karakter dengan kondisi background yang kompleks secara tepat terhadap 67 sampel citra dengan tingkat keberhasilan 95,52% untuk segmentasi plat dan 94,98% untuk segmentasi karakter dan metode template matching berhasil melakukan proses pengenalan karakter secara akurat dengan tingkat keberhasilan 87,45%. Kata kunci— real time, connected component analysis, template matching  Abstract Indonesia’s number plat recognition system are typically used in parking lots that are still done manually, by recording the license plate characters by parking guard. Though number plate recognition system is not only for parking but can be used to find vehicles that violate traffic rules highway street in real time, such as actors on the hit and run accident and the vehicles that violate traffic signs.This study provides an alternative car number plate character recognition using connected component analysis and matching so as to solve problems with complex background and a moving car on the road.Connected component analysis method successfully to the plates segmentation and character segmentation in complex background condition are appropriate to the 67 sample images with the success rate of 95.52% for the plate segmentation and 94.98% for plate character segmentation and template matching method successfully perform the character recognition process accurately with a success rate of 87.45%. Keywords— real time, connected component analysis, template matching

scholarly journals Automatic Receipt Recognition System Based on Artificial Intelligence Technology

2022 ◽  
Vol 12 (2) ◽  
pp. 853
Author(s):  
Cheng-Jian Lin ◽  
Yu-Cheng Liu ◽  
Chin-Ling Lee
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Character Segmentation ◽  
Small Object ◽  
Labor Costs ◽  
Accuracy Rate ◽  
Artificial Intelligence Technology ◽  
S Model

In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.

scholarly journals Comparative Study of Text Line Segmentation Algorithms on Low Quality Documents

2013 ◽  
pp. 270-276
Author(s):  
P. Soujanya ◽  
Vijaya Kumar Koppula ◽  
Kishore Gaddam
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Character Segmentation ◽  
Text Line ◽  
Run Length ◽  
Processing Step ◽  
Segmentation Algorithms ◽  
Text Line Segmentation ◽  
Line Segmentation

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

scholarly journals Character Segmentation Of Degraded Odia Script

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Ipsita Pattnaik ◽  
Tushar Patnaik
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
West Bengal ◽  
Input Image ◽  
Character Segmentation ◽  
Vertical Projection ◽  
Projection Profile ◽  
Regional Language ◽  
Optical Character ◽  
Line Segmentation

Optical Character Recognition (OCR) is a field which converts printed text into computer understandable format that is editable in nature. Odia is a regional language used in Odisha, West Bengal & Jharkhand. It is used by over forty million people and still counting. With such large dependency on a language makes it important, to preserve its script, get a digital editable version of odia script. We propose a framework that takes computer printed odia script image as an input & gives a computer readable & user editable format of same, which eventually recognizes the characters printed in input image. The system uses various techniques to improve the image & perform Line segmentation followed by word segmentation & finally character segmentation using horizontal & vertical projection profile.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Unconstrained Handwritten Text Line Segmentation for Kannada Language

2019 ◽  
Vol 8 (12) ◽  
pp. 953-956
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Text Line ◽  
Connected Component ◽  
Horizontal Projection ◽  
Text Documents ◽  
Handwritten Text ◽  
Kannada Language ◽  
System Separation ◽  
Line Segmentation

Segmentation is division of something into smaller parts and one of the Component of character recognition system. Separation of characters, words and lines are done in Segmentation from text documents. character recognition is a process which allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that the computer can use. the accuracy of OCR system is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. The main aim of this paper is to find out the various text line segmentations are Projection profiles, Weighted Bucket Method. Proposed method is horizontal projection profile and connected component method on Handwritten Kannada language. These methods are used for experimentation and finally comparing their accuracy and results.

Probabilistic Model Based Hybrid Classifier for Character Recognition

2017 ◽  
Vol 25 (04) ◽  
pp. 621-647
Author(s):  
A. K. Sampath ◽  
N. Gomathi
Keyword(s):  
Probabilistic Model ◽  
Character Recognition ◽  
Recognition Rate ◽  
Size Variation ◽  
Orientation Angle ◽  
Recognition System ◽  
Input Image ◽  
Hybrid Classifier ◽  
Form Processing ◽  
Model Based

Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

Research and Design of License Plate Recognition System

2014 ◽  
Vol 556-562 ◽  
pp. 2623-2627
Author(s):  
Feng Ran ◽  
Fa Yu Zhang ◽  
Mei Hua Xu
Keyword(s):  
Adaptive Learning ◽  
Character Recognition ◽  
Complete System ◽  
Recognition System ◽  
Morphological Processing ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Research And Design ◽  
Speed And Accuracy

Introduce a complete system of license plate recognition: using morphological processing and priori knowledge of license plate to discern the location of license plate, accomplishing tilt correction through Radon transform, then fulfilling character segmentation of accurate positioning license plate by projection, finishing character recognition through BP neural network which was improved by the use of adaptive learning rate and momentum factor. With the programming and verification on Matlab experimental platform, experimental results show that we can have a preferable recognition speed and accuracy.

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.

scholarly journals Character Segmentation and Recognition of Marathi Language

2021 ◽  
Vol 9 (12) ◽  
pp. 1544-1551
Author(s):  
Saurabh Ravindra Nikam
Keyword(s):  
Image Segmentation ◽  
Character Recognition ◽  
Structural Features ◽  
Input Image ◽  
Character Segmentation ◽  
Important Process ◽  
Critical Task ◽  
The Individual ◽  
Segmentation Image

Abstract: In this paper Segmentation is one the most important process which decides the success of character recognition fashion. Segmentation is used to putrefy an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. In segmentation image is partitioned into multiple corridor. With respect to the segmentation of handwritten words into characters it's a critical task because of complexity of structural features and kinds in writing styles. Due to this without segmentation these touching characters, it's delicate to fete the individual characters, hence arises the need for segmentation of touching characters in a word. Then we consider Marathi words and Marathi Numbers for segmentation. The algorithm is use for Segmentation of lines and also characters. The segmented characters are also stores in result variable. First it Separate the lines and also it Separate the characters from the input image. This procedure is repeated till end of train. Keywords: Image Segmentation, Handwritten Marathi Characters, Marathi Numbers, OCR.

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