scholarly journals Implementasi Optical Character Recognition Berbasis Backpropagation untuk Text to Speech Perangkat Android

2016 ◽  
Vol 6 (1) ◽  
pp. 13 ◽  
Author(s):  
Kristina Apriyanti ◽  
Triyogatama Wahyu Widodo
Keyword(s):  
Image Processing ◽  
Mobile Device ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Image Acquisition ◽  
Back Propagation ◽  
Processing System ◽  
Recognition System ◽  
Back Propagation Algorithm ◽  
Text To Speech

AbstrakProsedur penggunaan aplikasi text to speech pada perangkat mobile yang ada umumnya saat ini yakni pengguna aplikasi ini harus menginput manual kata yang akan diaktualisasikan dengan suara. Pada penelitian ini, dirancang sebuah sistem input kata pada aplikasi text to speech dengan memanfaatkan pengolahan citra digital. Pengguna cukup mengambil gambar (capture) kata yang akan disuarakan tersebut tanpa harus mengetik manual pada area teks input.             Metode yang digunakan dalam sistem ini meliputi akuisisi citra, pra pengolahan citra, segmentasi karakter, pengenalan karakter, dan integrasi dengan engine text to speech pada perangkat Android. Akuisisi citra dilakukan menggunakan kamera pada perangkat mobile untuk mengambil gambar kata yang akan diinputkan. Pengenalan karakter menggunakan jaringan saraf tiruan (JST) algoritma perambatan balik (back propagation). Sistem pengolahan citra yang berhasil dibuat kemudian dihubungkan dengan engine Google Text to Speech.             Sistem pengenalan karakter pada penelitian ini menggunakan model jaringan syaraf tiruan (JST) dengan akurasi 97,58%. Sistem ini mampu mengenali beberapa tipe font yakni Arial, Calibri, dan Verdana. Rerata akurasi pengenalan pada sampel uji yang digunakan di dalam penelitian ini sebesar 94,7% dengan kondisi jarak pengambilan gambar pada rentang jarak 3 – 8 cm dan posisi kamera tegak lurus menghadap kertas tulisan. Kata kunci— Android, OCR, Back Propagation, OpenCV, Text to Speech  Abstract Procedures using text to speech application on a mobile device generally at this time is user must manually enter the word to be actualized in speech. In this study, designed a words input system for text to speech application using digital image processing. This system makes users simply to do the words capturing that will be voiced without manually typing in the text area input.The method used in this system includes image acquisition, image pre-processing, character segmentation, character recognition, and integration with text to speech engine on mobile devices. Image acquisition was performed using the camera on a mobile device to capture the word to be entered. Character recognition using back propagation algorithm. Image processing system successfully created and then integrated with Google Text to Speech engine.Character recognition system in this study using a model of neural networks (ANN) with an accuracy of 97.58%. The system is able to recognize some types of font that is Arial, Calibri, and Verdana. The mean recognition accuracy on the test sample used in this study 94.7% with distance shooting conditions within the range 3-8 cm and the camera upright position facing the letter. Keywords— Android, OCR, Back Propagation, OpenCV, Text to Speech

scholarly journals License Plate Recognition for Vehicle Surveillance

2020 ◽  
pp. 134-138
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Color Contrast ◽  
License Plate ◽  
License Plate Recognition ◽  
Optical Character

License plate recognition system plays very important role in various security aspects which includes entry monitoring of a particular vehicle in commercial complex, traffic monitoring , identification of threats and many more. In past few years many different methods has been adopted for license plate recognition system but still there is little more chance to work on real time difficulties which come across while license plate recognition like speed of vehicle, angle of license plate in picture, background of picture or color contrast of image, reflection on the license plate and so on. The combination of object detection, image processing, and pattern recognition are used to fulfill this application. In the proposed architecture , system will capture a small video and using Google's OCR(Optical Character Recognition) system will recognize license number, if that number get found in database gate will get open with the help of Arduino Uno.

scholarly journals Vehicle Number Plate Recognition using Micro Processor

2019 ◽  
Vol 8 (12) ◽  
pp. 3682-3684
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Processing Technology ◽  
License Plate ◽  
Text Format ◽  
Optical Character ◽  
Plate Number ◽  
Image Processing Technology

An automatic license number plate recognition system that uses image processing technology for identifying the written characters and numbers on the vehicle’ license plate. The system can be used in highly secured areas to provide more safety, and can be used in parking, traffic, and other places to monitor all vehicle’s number plate in a predefined area. The character is recognized by the OCR technology that is optical character recognition system. It generates the vehicle’s license plate number in a text format. The recognized number from the license plate then can be used to retrieve more information about the vehicle and the owner.

scholarly journals Image Enhancement of Complex Document Images Using Histogram of Gradient Features

2018 ◽  
Vol 7 (4.36) ◽  
pp. 780
Author(s):  
Sajan A. Jain ◽  
N. Shobha Rani ◽  
N. Chandan
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Processing System ◽  
Recognition System ◽  
Document Images ◽  
Uniform Illumination ◽  
Optical Character ◽  
Research Challenge

Enhancement of document images is an interesting research challenge in the process of character recognition. It is quite significant to have a document with uniform illumination gradient to achieve higher recognition accuracies through a document processing system like Optical Character Recognition (OCR). Complex document images are one of the varied image categories that are difficult to process compared to other types of images. It is the quality of document that decides the precision of a character recognition system. Hence transforming the complex document images to a uniform illumination gradient is foreseen. In the proposed research, ancient document images of UMIACS Tobacco 800 database are considered for removal of marginal noise. The proposed technique carries out the block wise interpretation of document contents to remove the marginal noise that is present usually at the borders of images. Further, Hu moment’s features are computed for the detection of marginal noise in every block. An empirical analysis is carried out for classification of blocks into noisy or non-noisy and the outcomes produced by algorithm are satisfactory and feasible for subsequent analysis. 

scholarly journals Two-Dimensional Optical Character Recognition of Mouse Drawn in Turkish Capital Letters Using Multi-Layer Perceptron Classification

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Ehsan Ali Al-Zubaidi ◽  
Maad M. Mijwil ◽  
Aysar Sh. Alsaadi
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Back Propagation ◽  
Text Recognition ◽  
Back Propagation Algorithm ◽  
Digital Text ◽  
Development Environment ◽  
Optical Character ◽  
Printed Materials

The Optical Character Recognition (OCR) is software for text recognition that takes an image containing text, to transform it into strings, then save them into a format that make it able to use in text editing programs. The OCR plays a significant role in the transformation of printed materials into digital text files. These digital files can be very useful for children and adults who have awkward reading. This is because a digital text can be used with computer programs that allow people to read them in different ways. In this paper, we developed system for Turkish character recognition under visual studio (C#) development environment, where machine learning is used to accurately predict optical characters, the reason why it has a high precision and high recognition speed through deep learning, which is one of the machine learning methods for OCR when drawing letters by mouse on the screen, then recognize by using back propagation algorithm.

scholarly journals Automated License Plate Recognition System using Computer Vision

2019 ◽  
Vol 8 (6) ◽  
pp. 1878-1881
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Digital Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Digital Processing ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Optical Character

This paper discusses about License plate recognition using digital processing of images, where the image of a vehicle is taken and the number plate is then recognized by various layers of digital image processing. The number plate is then allowed to undergo optical character recognition (OCR), this extracts the data and then compares it with a database containing the details of the vehicle. This allows the user to identify the type of vehicle and the identity of the person who is driving the vehicle. It will denote the user about the registration of the vehicle by comparing it with the database of the registered vehicle in the area. The device will consist of a camera which will take the real time footage of the vehicles and a snap from the video of the vehicle is used to recognize the number plate. The processor will process the images and will display the number of the vehicle and the owner of the vehicle in the display, this is achieved by comparing the number of the vehicle with the previously fed data from the database. This device will provide an efficient way for automating a parking system where there will be no need for a human to interfere with the checking of the vehicle and providing passes for the vehicle.

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