Automatic Number Plate Recognition Market - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015 - 2023

NEW YORK, Jan. 18, 2016 /PRNewswire/ -- Automatic Number Plate Recognition (ANPR) is a surveillance system with primary capability of capturing the image of vehicles and identifies their license number. ANPR systems consist of high-speed cameras with IR filters, processors capable of performing optical character recognition (OCR), application software, user interface, and an alert capability to notify operators in case of a match. ANPR systems can be a standalone device or can be used with existing traffic enforcement cameras or closed-circuit televisions and are used by the police forces across the globe to prevent and detect crimes. These systems are also used for electronic toll collection on roads and for monitoring traffic activities such as red light adherence and speed enforcement. Additionally, ANPR systems can assist in the detection of stolen vehicles. The ANPR technology varies from location to location depending on the designs of number plates.

ANPR systems are increasingly being adopted in traffic enforcement, detection, and prevention of crimes and access control in the public sector. Initiatives undertaken by governments to incorporate new technologies in order to ensure safety in the public sector is one of the key factors driving growth in the global automatic number plate recognition market. However, challenges related to non-uniformity in the license plate design across different states, countries, or regions are hindering the adoption of this technology. Applications of ANPR systems in the commercial sector have increased in the recent years and shown significant traction in the vehicle-parking segment.

This market has been segmented based on applications, end-users and geography. Based on the end-users adoption, the market has been classified into government and commercial sectors. The government sector is further classified into homeland security, traffic department and defense, whereas the commercial end-users are classified into entertainment and recreation, and dedicated car parks. The market size and forecasts in terms of revenue (USD Million) for each of these segments have been provided for the period 2015 to 2023, considering 2013 and 2014 as the base years. The report also provides the compounded annual growth rate (CAGR) for each segment of the market for the forecast period 2015 to 2023. Geographically, the market for ANPR has been segmented into four regions, namely, North America, Europe, Asia-Pacific, Middle East & Africa (MEA) and Latin America.

The report includes value chain analysis to recognize players in the market along with their role in the ecosystem. We have included future trends that will impact the demand. The study provides market estimation in terms of revenue and forecast for the period 2015 to 2023.

Some of the key players in the global ANPR market include ARH Inc., Digital Recognition Systems Ltd., ELSAG North America LLC, NDI Recognition Systems Ltd., Q-Free ASA, Vigilant Solutions Inc., Siemens AG, PIPS Technology Ltd., Genetec, Inc., Bosch Security Systems, CA Traffic Limited, COBAN Technologies, Quercus Technologies, Tattile Srl and Vysionics Intelligent Traffic Solutions Ltd.

The report analyzes the global automatic number plate recognition (ANPR) market in terms of revenue (USD million). The market has been segmented as follows:
Automatic Number Plate Recognition (ANPR) Market, by Applications:
Security and Surveillance
Vehicle Parking
Traffic Management
Red Light Control
Toll Enforcement

Automatic Number Plate Recognition (ANPR) Market, by Ends-user:
Government
Homeland Security
Traffic Department
Defense
Commercial
Entertainment and Recreation Facilities (Shopping Centers, Multiplexes and Others)
Dedicated Car Parks

Automatic Number Plate Recognition (ANPR) Market, by Geography:
North America
Europe
Asia-Pacific
Middle East & Africa (MEA)
Latin America
Read the full report: http://www.reportlinker.com/p03564636-summary/view-report.html

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