English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Iraqi License Plate Recognition (ILPR) is useful in many fields, such as private or public exits, parking lots, traffic control, and theft detection. This paper focusing on detecting the plate car and identifying the Arabic number on the plate. The main paper goal was to identify and understand plate cars with numbers / characters in the plate where it was difficult to detect the same object with different environments. It also, offers LPR which consists of three main stages; pre-processing with detect license plates, segmentation and recognition of characters. the first stage takes a camera picture then preprocessing it. License plate detection check for matching license plate to crop the appropriate plate in the picture. Segmentation done separately by splitting the numbers. The final stage is number recognition by using KNearest Neighbours (KNN) which is one of the basic machine learning algorithms used to match numbers with training data to provide a prediction that is accurate. The system was implemented using python3.5, opencv library and, using 50 images, the accuracy performance results equal to 90 percent.

IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING