scholarly journals Embedded system design to control the entry and exit of vehicles online, at the main access of ESPOCH

2018 ◽  
Vol 3 (CITT2017) ◽  
pp. 113-120
Author(s):  
Javier J. Gavilanes ◽  
Jairo R. Jácome ◽  
Alexandra O. Pazmiño
Keyword(s):  
Embedded System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Entry And Exit ◽  
Real Time System ◽  
Optical Character ◽  
Nearest Neighbours ◽  
Processing Techniques ◽  
Python Programming ◽  
Main Entrance

In this research a embedded real-time system was developed by using Raspberry Pi3 (a reduced board computer), which is an equipment with a camera placed in strategic points of the mechanic arms at the main entrance and exit of Escuela Superior Politécnica de Chimborazo, this equipment captures images of vehicles that enter and exit the campus and the information is extracted through the implementation of a segmentation algorithm written in Python programming language and the collaboration of artificial vision bookstores offered by OpenCV, processing techniques were applied to extract the vehicle plate from the location scenery. Then, an Optical Character Recognition (OCR) algorithm also known as K-Nearest Neighbours (KNN) was applied, which after a training phase is able to identify letters and numbers on the automobile plates, the information is stored in the entrance database and it is deleted when the automobile exits the campus.

scholarly journals Embedded system design to control the entry and exit of vehicles online, at the main access of ESPOCH

2018 ◽  
Vol 3 (CITT2017) ◽  
pp. 113-121
Author(s):  
Javier J. Gavilanes ◽  
Jairo R. Jácome ◽  
Alexandra O. Pazmiño
Keyword(s):  
Embedded System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Entry And Exit ◽  
Real Time System ◽  
Optical Character ◽  
Nearest Neighbours ◽  
Processing Techniques ◽  
Python Programming ◽  
Main Entrance

In this research a embedded real-time system was developed by using Raspberry Pi3 (a reduced board computer), which is an equipment with a camera placed in strategic points of the mechanic arms at the main entrance and exit of Escuela Superior Politécnica de Chimborazo, this equipment captures images of vehicles that enter and exit the campus and the information is extracted through the implementation of a segmentation algorithm written in Python programming language and the collaboration of artificial vision bookstores offered by OpenCV, processing techniques were applied to extract the vehicle plate from the location scenery. Then, an Optical Character Recognition (OCR) algorithm also known as K-Nearest Neighbours (KNN) was applied, which after a training phase is able to identify letters and numbers on the automobile plates, the information is stored in the entrance database and it is deleted when the automobile exits the campus.

Arabic Optical Character Recognition

2011 ◽  
pp. 324-346 ◽  
Author(s):  
Husni Al-Muhtaseb ◽  
Rami Qahwaji
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Arabic Language ◽  
Text Recognition ◽  
Text Segmentation ◽  
Future Trends ◽  
Optical Character ◽  
Arabic Ocr ◽  
Processing Techniques ◽  
Arabic Speaking

Arabic text recognition is receiving more attentions from both Arabic and non-Arabic-speaking researchers. This chapter provides a general overview of the state-of-the-art in Arabic Optical Character Recognition (OCR) and the associated text recognition technology. It also investigates the characteristics of the Arabic language with respect to OCR and discusses related research on the different phases of text recognition including: pre-processing and text segmentation, common feature extraction techniques, classification methods and post-processing techniques. Moreover, the chapter discusses the available databases for Arabic OCR research and lists the available commercial Software. Finally, it explores the challenges related to Arabic OCR and discusses possible future trends.

scholarly journals Automatic Number Plate Recognition System Using Optical Character Recognition

2019 ◽  
pp. 986-992 ◽  
Author(s):  
Monika Arora ◽  
Anubha Jain ◽  
Shubham Rustagi ◽  
Tushar Yadav
Keyword(s):  
Embedded System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Monitoring And Control ◽  
Monitoring And Control System ◽  
Vehicle Owner ◽  
Optical Character ◽  
And Control

In the last few decades, the number of active vehicle population has increased drastically which has made it difficult for the authorities to keep a track of them as well as to identify the vehicle owner in case of any traffic violation. Automatic Number Plate Recognition System (ANPR) is a real-time machine-intelligent and embedded system which identifies the characters directly from the image of the number plate. Due to crucial research and development of technology and the increasing use of vehicles, the need for a machine-oriented recognition and monitoring system is of immense importance. The technology has become a major requirement and is playing a crucial role in a vast sea of applications related to automated transport monitoring and control system such as traffic monitoring, challan management, detection of stolen vehicles, electronic payment of tolls on highways or bridges, parking lots access control, etc. This technology requires extensive mobility and station flexibility which causes it to be installed on such hardware that is very mobile enough so that the operator can use it very efficiently. ANPR System through the use of Optical Character Recognition (OCR) makes the system to be used as an application on smartphones. This provides the operator to use the system and identify number plates by just capturing the image and processing by neural networks working in the background of OCR. The ANPR system as a whole will result in easy and safe monitoring of the traffic and to keep an easy record in case of any violation. Also, it will save individuals to save their time in standing at long queues at toll taxes and paying cash which will be done with the ANPR system and using E-wallet.

Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 77-93 ◽  
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

Use of Deep Neural Network for Optical Character Recognition

2020 ◽  
pp. 219-254
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Comparative Study ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Detailed Review ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

In this chapter, the authors have given a detailed review on optical character recognition. Various methods are used in this field with different accuracy levels. Still there are some difficulties in recognizing handwritten characters because of different writing styles of different individuals even in a particular language. A comparative study is given to understand different types of optical character recognition along with different methods used in each type. Implementation of neural network in different forms is found in most of the works. Different image processing techniques like OCR with CNN, RNN, combination of CNN and RNN, etc. are observed in recent research works.

Hindi Optical Character Recognition and Its Applications

2017 ◽  
pp. 28-39
Author(s):  
Rashmi Gupta ◽  
Dipti Gupta ◽  
Megha Dua ◽  
Manju Khari
Keyword(s):  
Computer Vision ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwritten Documents ◽  
Library Automation ◽  
Optical Character ◽  
Machine Printed Documents ◽  
Processing Techniques ◽  
Handwritten Recognition ◽  
Hand Writing

Recognition is an important part in the computer vision. Optical character recognition is nowadays gaining its importance in terms of the digital and handwritten documents recognition. Devanagari is widely spoken script with more than 300 million people relying on it for their day-to-day activities, so recognition of Devanagari characters is gaining its importance in the recent times. Tasksin handwritten recognition handle the differences along with alteration of Hindi characters written in offline mode. Furthermore, Hindi character are written in different sizes shapes and orientation in contrast to hand writing usually written along a particular baseline in a horizontal direction. Handwritten and machine printed documents are needed to be recognized for the applications like bank Cheque processing, library automation, publication house, manuscripts, Granths and other forms and documents. In this paper an attempt has been made to shortlist the methods and processing techniques studied so far in the field of Devanagari character recognition. The performance analysis and the results for the various techniques are given in the chapter.

Forgery document detection in information management system using cognitive techniques

2020 ◽  
Vol 39 (6) ◽  
pp. 8057-8068
Author(s):  
Mohamed Sirajudeen ◽  
R. Anitha
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Information Management System ◽  
Detection Methods ◽  
Detection Model ◽  
Identity Card ◽  
Processing Techniques ◽  
Python Programming ◽  
Verification Model

Manually verifying the authenticity of the physical documents (personal identity card, certificates, passports, legal documents) increases the administrative overhead and takes a lot of time. Later image processing techniques were used. But most of the image processing based forgery document detection methods are less accurate. To improve the accuracy, this paper proposes an automatic document verification model using Convolutional Neural Networks (CNN). Furthermore, we use Optical Character Recognition (OCR) and Linear Binary Pattern (LBP) to extract the textual information and regional edges from the documents. Later, Oriented fast and Rotated Brief (ORB) is used to extract the images from the scanned documents. To train the CNN, MIDV-500 dataset of 256 Azerbaijani passport images, each with the size of 1040*744 pixels is taken. The proposed CNN model uses sliding window operations layers to evaluate the authenticity. The proposed model analyzes both the textual authenticity and image (seal, stamp and hologram) authenticity of the scanned document. The experimental analysis is carried out on the TensorFlow using python programming language. The results derived from the proposed CNN based forgery detection model is compared with existing models and the results are promising to implement on the real time applications

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

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