scholarly journals Table Recognition for Sensitive Data Perception in an IoT Vision Environment

2019 ◽  
Vol 9 (19) ◽  
pp. 4162 ◽  
Author(s):  
Jin Zhang ◽  
Yanmiao Xie ◽  
Weilai Liu ◽  
Xiaoli Gong
Keyword(s):  
Mobile Phones ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Search Algorithm ◽  
Perspective Transformation ◽  
Physical World ◽  
High Definition ◽  
Sensitive Data ◽  
Table Recognition ◽  
Iot Devices

Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.

scholarly journals An Efficient Approach for Vehicle Number Plate Recognition in Pakistan

2020 ◽  
Vol 06 (1) ◽  
pp. 12-21
Author(s):  
Saif Ur Rehman ◽  
Moiz Ahmad ◽  
Asif Nawaz ◽  
Tariq Ali
Keyword(s):  
Real Time ◽  
Homeland Security ◽  
Traffic Control ◽  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Road Traffic ◽  
High Definition ◽  
Control Center ◽  
Matching Mechanism

Introduction: Recognition of Vehicle License Number Plates (VLNP) is an important task. It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. An ANPR (Automatic Number Plate Recognition) is a system in which the image of the vehicle is captured through high definition cameras. The image is then used to detect vehicles of any type (car, van, bus, truck, and bike, etc.), its’ color (white, black, blue, etc.), and its’ model (Toyota Corolla, Honda Civic etc.). Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing. Aim: ANPR is a challenging problem, especially when the number plates have varying sizes, the number of lines, fonts, background diversity, etc. Different ANPR systems have been suggested for different countries, including Iran, Malaysia, and France. However, only a limited work exists for Pakistan vehicles. Therefore, in this study, we aim to propose a novel ANPR framework for Pakistan VLNP recognition. Methods: The proposed ANPR system functions in three different steps: (i) - Number Plate Localization (NPL); (ii)- Character Segmentation (CS); and (iii)- Optical Character Recognition (OCR), involving template-matching mechanism. The proposed ANPR approach scans the number plate and instantly checks against database records of vehicles of interest. It can further extract the real=time information of driver and vehicle, for instance, license of the driver and token taxes of vehicles are paid or not, etc. Results: Finally, the proposed ANPR system has been evaluated on several real-time images from various formats of number plates practiced in Pakistan territory. In addition to this, the proposed ANPR system has been compared with the existing ANPR systems proposed specifically for Pakistani licensed number plates. Conclusion: The proposed ANPR Model has both time and money-saving profit for law enforcement agencies and private organizations for improving homeland security. There is a need to expand the types of vehicles that can be detected: trucks, buses, scooters, bikes. This technology can be further improved to detect the crashed vehicle’s number plate in an accident and alert the closest hospital and police station about the accident, thus saving lives.

scholarly journals Development of Optical Character Recognition Software Package for Mobile Phones

2010 ◽  
pp. 99-104
Author(s):  
Sukhwant Kaur ◽  
H. K. Kaura ◽  
Mritunjay Ojha
Keyword(s):  
Mobile Phones ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
General Purpose ◽  
Desktop Computer ◽  
Text Format ◽  
Software Packages ◽  
Optical Character ◽  
Recognition Software ◽  
Day By Day

Optical Character Recognition (OCR) is a technique through which any textual information contained in images are extracted and converted into editable text format. The various OCR software packages which are available in desktop computer with scanner suffer from one primary constraint- MOBILITY. We have developed an OCR application for mobile phones. All the procedures needed for extracting the text would be performed within the mobile phone, eliminating the need for bulky devices like scanners, desktops and also laptops. Hence it would provide the user the much needed ‘anywhere, anytime’ feature for OCR. The computational power of mobiles is increasing day by day making it easier to run image processing operations for OCR application. Also the resolution of camera in mobile is increasing to match the resolution of scanners. After the document is processed, it can be communicated to another user by email facility of mobile phones as text files. The aim of this paper is to investigate the various issues involved in developing this OCR application in mobile phones. Further design and future scope for this application is elaborated giving insight to the development process. The motivation here was to provide a general purpose framework for OCR application in mobile phones. The framework is developed in a modular fashion.

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.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

Pengantar dan Survey Tentang Optical Music Recognition

2014 ◽  
Vol 6 (1) ◽  
pp. 36-39
Author(s):  
Kevin Purwito
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Music Recognition ◽  
Optical Character ◽  
Digital Format ◽  
Music Recognition ◽  
Index Terms ◽  
The Many ◽  
Further Development ◽  
Music Symbol

This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, namely Preprocessing, Music Symbol Recognition, Musical Notation Reconstruction and Final Representation Construction. Each of those four main processes uses different methods and algorithms and each of those processes still needs further development and research. There are already many application that uses OMR to date, but none gives the perfect result. Therefore, besides the development and research for each OMR process, there is also a need to a development and research for combined recognizer, that combines the results from different OMR application to increase the final result’s accuracy. Index Terms—Music, optical character recognition, optical music recognition, musical symbol, image processing, combined recognizer  

Sign in / Sign up

Export Citation Format

Share Document