scholarly journals Optical Character Recognition Robot

10.29007/jx6c ◽  
2018 ◽  
Author(s):  
Deepak Vala ◽  
Umeshkumar Baria ◽  
Urvi Bhagat ◽  
Mohan Khambalkar
Keyword(s):  
Open Source Software ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Local Network ◽  
Line Of Sight ◽  
Raspberry Pi ◽  
Plain Text ◽  
Optical Character ◽  
Readable Text ◽  
Non Line Of Sight

In this paper presents optical character recognition robot (OCR) which is capable of converting image into the computer process able format, in the form of plain text using Raspberry pi and a webcam server where we can live stream video over a local network. Our ultimate goal is to find and solve the different requirements in making a web controlled robot that recognizes and converts textual messages placed in real world to the computer readable text files. Our objective is to integrate the appropriate techniques to explain and prove that such capability, using limited hardware and software capabilities. The objective of our work is to provide an internet controlled mobile robot with the capability of reading characters in the image and gives out strings of characters. In the project we will use MOTION software, which is open source software with a number of configuration options which can be changed according to our needs. Here configurations are to be made so that it allows you to view from any computer on the local network for the control of robot in non-line of sight areas.

scholarly journals Camera-Based Parking System Management Using Raspberry Pi

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Ahmad Zatnika Purwalaksana ◽  
Dewi Siburian ◽  
Immanuel Sianturi ◽  
Sabam Sianturi
Keyword(s):  
Data Storage ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Raspberry Pi ◽  
System Management ◽  
Data Recording ◽  
Optical Character ◽  
Vehicle Data ◽  
Parking System ◽  
One Time Password

Currently, there are still many use of manual parking systems, namely parking attendants provide instructions for vehicles to park. This is often considered inefficient because there is no data recording of vehicles that park, resulting in a low level of security and comfort for visitors and the parking system does not provide information about parking slots which can make it difficult for visitors to park vehicles. With this, the vehicle that wants to park often has difficulty when parking the vehicle. Through various problems that occur, the author develops a parking system that can provide information on the availability of parking slots in the parking area as well as data storage for vehicle number plates that do parking. In the parking system, the Raspberry Pi 3 Model B is used as the main controller, camera detection to obtain information in the form of characters from the vehicle number plate with the help of OCR (Optical Character Recognition), the use of OTP (One Time Password) code which can be used only once so as to increase security. on the parking system. Vehicle data in the form of number plates and also OTP code will be stored in the database and used when the vehicle will leave the parking area by matching the number plate data and OTP code of a vehicle to be able to leave the parking area. Through the development of the parking system, it is hoped that it will work well for vehicle drivers to find available parking locations and increase safety and comfort for drivers because of data storage in the form of vehicle number plates as vehicle identity and the use of OTP codes that can only be used once.

scholarly journals Implementation of Optical Character Recognition Using Raspberry Pi for Visually Challenged Person

2018 ◽  
Vol 7 (3.34) ◽  
pp. 65 ◽  
Author(s):  
S Thiyagarajan ◽  
Dr G.Saravana Kumar ◽  
E Praveen Kumar ◽  
G Sakana
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Native Speaker ◽  
Image Data ◽  
Raspberry Pi ◽  
Spoken Discourse ◽  
Optical Character ◽  
Digital Format ◽  
Visually Challenged

Blind people are unable to perform visual tasks. The majority of published printed works does not include Braille or audio versions, and digital versions are still a minority. In this project, the technology of optical character recognition (OCR) enables the recognition of texts from image data. The system is constituted by the raspberry pi, HD camera and Bluetooth headset. This technology has been widely used in scanned or photographed documents, converting them into electronic copies. The technology of speech synthesis (TTS) enables a text in digital format to be synthesized into human voice and played through an audio system. The objective of the TTS is the automatic conversion of sentences, without restrictions, into spoken discourse in a natural language, resembling the spoken form of the same text, by a native speaker of the language.  

scholarly journals Identifikasi Plat Nomor Kendaraan Dengan Metode Optical Character Recognition Menggunakan Raspberry Pi

2020 ◽  
Vol 7 (2) ◽  
pp. 116-125
Author(s):  
Winarno Sugeng ◽  
Rio Korio Utoro ◽  
Mochamad Tegar Prabowo
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Raspberry Pi ◽  
Error Handling ◽  
Optical Character

Plat Nomor Kendaraan merupakan identitas bagi setiap kendaraan bermotor yang terdaftar oleh pemerintah Indonesia. Proses identifikasi plat nomor diawali dengan pembambilan gambar kendaraan dengan kamera. Gambar akan di resize untuk menyamakan ukuran citra dan crop untuk memisahkan antara plat nomor dengan bagian kendaraan lainnya menggunakan algoritma Perspective Transform, setelah itu di crop kembali untuk memisahkan antara kode wilayah, kode registrasi dan kode seri wilayah, lalu setiap karakter akan dikenali menggunakan metode Optical Character Recognition (OCR) berdasarkan citra karakter hasil crop. Terdapat proses character error handling untuk meningkatkan tingkat akurasi identifikasi pada karakter plat nomor. Plat nomor Indonesia yang diujikan terbagi menjadi 4 kategori yaitu Plat Standar Mobil, Plat Kustom Mobil, Plat Tidak Standar dan Plat Motor. Resolusi kamera terbaik menggunakan resolusi 1280x720 piksel dan menghasilkan rata-rata waktu uji 6,51 detik. Persentase kebenaran identifikasi karakter terbesar untuk plat standar sebesar 100% pada nilai lux 20~39 dan nilai lux 70~99, sedangkan untuk plat nomor kustom persentase kebenaran identifikasi karakter terbesar sebesar 58% pada nilai lux 90~140. Sedangkan untuk plat tidak standar tidak ada persentase kebenaran identifikasi karakter terbesar menghasilkan persentase 0% pada semua nilai lux yang diujikan. Untuk plat nomor motor persentase kebenaran identifikasi karakter terbesar sebesar 8% pada nilai lux 150~199. Sehingga dapat ditarik kesimpulan bahwa plat nomor standar menjadi rekomendasi yang tepat bagi setiap kendaraan bermotor yang ada di Indonesia.

scholarly journals Rancang Bangun Palang Otomatis Dengan Pengenalan Plat Kendaraan Menggunakan OpenALPR

2020 ◽  
Vol 5 (4) ◽  
pp. 36
Author(s):  
Rahmat Darmawan ◽  
Ahmad Taqwa ◽  
Jon Endri
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Raspberry Pi ◽  
Optical Character

Dikarenakan meningkatnya penggunaan lahan parkir dan keterbatasan lahan, maka seringkali lahan parkir tidak digunakan secara tepat. Namun masalah tersebut dapat diatasi dengan cara mengimplementasikan sistem palang otomatis dengan pendeteksian plat nomor kendaraan berbasis raspberry pi. Proses pengenalan plat nomor kendaraan dilakukan dengan metode optical character recognition (ocr). Hasil pada tugas akhir ini akan menampilkan plat yang telah terdeteksi dan merespon ke output yang disediakan.

scholarly journals Overall Equipment Utilisation (OEU) Monitoring and Remote Quality Check in Legacy Machine with Raspberry Pi

2021 ◽  
Vol 1 (2) ◽  
pp. 135-144
Author(s):  
Siti Nurul Huda Abd Rahim ◽  
Abd Halim Embong
Keyword(s):  
Neural Network ◽  
Graphical User Interface ◽  
Error Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Raspberry Pi ◽  
Manufacturing Companies ◽  
Optical Character ◽  
Quality Check

Overall Equipment Utilisation (OEU) plays an important role as a benchmark for manufacturing companies to determine each machine's efficiency. Currently, there is no proper OEU measurement system in legacy machines and only relies on human observation. This project aims to develop a measurement of OEU system by using Optical Character Recognition (OCR). An efficient Optical Character Recognition (OCR) algorithm is needed to have a high percentage of recognition rate. The outcome of this project will be a Graphical User Interface (GUI) that display real-time OEU monitoring and remote quality check for legacy machines. Pytesseract-OCR Version 4 classifier using the Recurrent Neural Network (RNN) method has been proposed in this paper. Furthermore, an error detection feature is designed from OCR output.

Real Time Implementation of Optical Character Recognition Based TTS System using Raspberry pi

2017 ◽  
Vol 7 (7) ◽  
pp. 149
Author(s):  
Sagar G. K ◽  
Shreekanth T
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
World Health ◽  
Raspberry Pi ◽  
Blind People ◽  
Poor Vision ◽  
Input Text ◽  
Optical Character ◽  
Health Organization ◽  
Text Page

Thetext to speech (TTS) conversion technology is proposed to help the blind people and people with poor vision. According to survey done by World Health Organization (WHO) there are about 286 million blind people in this world and about 91% of them reside in developing countries. So there is necessity of portable TTS converter which should be affordable to help the blinds. To help the blind community a smart reader is proposed in this paper. It includes a web cam to capture input text page which is then processed by TTS unit installed in raspberry pi and the output is then amplified by audio and given out on speaker.

scholarly journals A Smart Reader for Blind People

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1566-1568
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Visually Impaired ◽  
Raspberry Pi ◽  
Human Communication ◽  
Human Needs ◽  
Normal People ◽  
Optical Character ◽  
Visually Impaired People ◽  
Visually Challenged

This paper presents an intelligent bot for aiding the visually challenged people. Presently, 81% are visually impaired who live in the developing countries. Nowadays Human communication is mainly focused on text and speech. To read the text a human needs a vision. Survey conducted on several papers and systems provides hardware consisting of a camera interface with Raspberry Pi for processing the text. The camera captures text image of a handwritten or printed text. The raspberry pi makes use of Optical Character Recognition (OCR) software installed in it, to perform the conversion of an image to text and similarly text to speech conversion. The assistant is applicable for visually impaired people as well as for normal people in order to increase their level of comfort.

scholarly journals Smart Glass for Visually Challenged Peoples to Read the Books using Raspberry Pi

2021 ◽  
pp. 258-262
Author(s):  
Anitha D B ◽  
Jyothi T M ◽  
Pooja R ◽  
Sahana N
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Computer Software ◽  
Raspberry Pi ◽  
Text To Speech ◽  
Smart Glasses ◽  
Optical Character ◽  
Text Reading ◽  
Visually Challenged ◽  
Audio Output

The objective of this paper is to presents new design on assistive smart glasses for visually impaired. The objective is to assist in multiple daily tasks using the advantage of wearable design format. The proposed method is a camera based assistive text reading to help to blind in person in reading the text present on the text labels, printed notes and products in their own respective languages. It combines the concept of Optical Character Recognition (OCR), text to Speech Synthesizer (TTS) and translator in Raspberry pi. Optical character recognition (OCR) is the identification of printed characters using photoelectric devices and computer software. It converts images of typed, handwritten or printed text into machine encoded text from scanned document or from subtitle text superimposed on an image. Text-to-Speech conversion is a method that scans and reads any language letters and numbers that are in the image using OCR technique and then translates it into any desired language and at last it gives audio output of the translated text. The audio output is heard through the raspberry pi's audio jack using speakers or earphones.

scholarly journals Optimalisasi Image Thresholding pada Optical Character Recognition Pada Sistem Digitalisasi dan Pencarian Dokumen

Petir ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 1-11
Author(s):  
Ridwan Rismanto ◽  
Arief Prasetyo ◽  
Dyah Ayu Irawati
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Threshold Value ◽  
Document Image ◽  
Great Effort ◽  
Processing Stage ◽  
Image Thresholding ◽  
Optical Character ◽  
Value Sets ◽  
Readable Text

The administration activity in an institute is largerly done by using a paper based mailing and document as a media. Therefore, a great effort needs to be performed in the case of management and archiving, in the form of providing storage space through the categorizing system. Digitalization of document by scanning it into a digital image is one of the solution to reduce the effort to perform the work of archiving and categorizing such document. It also provide searching feature in the form of metadata, that is manually written during the digitalization process. The metadata can contains the title of document, summary, or category. The needs to manually input this metadata can be solved by utilizing Optical Character Recognition (OCR) that converts any text in the document into readable text storing in the database system. This research focused on the implementation of the OCR system to extract text in the scanned document image and performing optimization of the pre-processing stage which is Image Thresholding. The aim of the optimization is to increase OCR accuracy by tuning threshold value of given value sets, and resulting 0.6 as the best thresholding value. Experiment performed by processing text extraction towards several scanned document and achieving accuration rate of 92.568%.

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