ALPR and ANPR Cameras combined analysis in this report. Automated License Plate Recognition (ALPR) is a technology that uses optical character recognition (OCR) to automatically read license plate characters. There are two types of ALPR: stationary, which uses infrared (IR) cameras at high fixed points, and mobile, which uses vehicle-mounted IR cameras. Market Analysis and Insights: Global ALPR and ANPR Cameras Market The global ALPR and ANPR Cameras market was valued at US$ XX in 2020 and will reach US$ XX million by the end of 2027, growing at a CAGR of XX% during 2022-2027. Global ALPR and ANPR Cameras Scope and Market Size The global ALPR and ANPR Cameras market is segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global ALPR and ANPR Cameras market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on sales, revenue and forecast by region (country), by Type and by Application for the period 2016-2027. Segment by Type Stationary Type Mobile Type Segment by Application Parking Lots Entrance or Exit Toll Gate Parking Spot City Surveillance Others The ALPR and ANPR Cameras market is analysed and market size information is provided by regions (countries). Segment by Application, the ALPR and ANPR Cameras market is segmented into North America, Europe, China, Japan, Southeast Asia, India and Other Regions. By Company PIPS Technology Basler Inex Technologies ACTi Corporation MAV Systems Limited Riverland Technologies ARH Inc Genetec Inc Vivotek Tattile s.r.l. Cass Parking Merit LILIN Ent. Co., Ltd Axis Communications Arvoo BV Bosch 3M DivioTec Inc Jenoptik
1 ALPR and ANPR Cameras Market Overview 1.1 ALPR and ANPR Cameras Product Scope 1.2 ALPR and ANPR Cameras Segment by Type 1.2.1 Global ALPR and ANPR Cameras Sales by Type (2016 & 2021 & 2027) 1.2.2 Stationary Type 1.2.3 Mobile Type 1.3 ALPR and ANPR Cameras Segment by Application 1.3.1 Global ALPR and ANPR Cameras Sales Comparison by Application (2016 &
