3 research outputs found
Text localisation for roman words from shop signage / Nurbaity Sabri ... [et al.]
Text localisation determinesthe location of the text in an image. This process
is performed prior to text recognition. Localising text on shop signage is
a challenging task since the images of the shop signage consist of complex
background, and the text occurs in various font types, sizes, and colours.
Two popular texture features that have been applied to localise text in
scene images are a histogram of oriented gradient (HOG) and speeded up
robust features (SURF). A comparative study is conducted in this paper
to determine which is better with support vector machine (SVM) classifier.
The performance of SVM is influenced by its kernel function and another
comparative study is conducted to identify the best kernel function. The
experiments have been conducted using primary data collected by the
authors. Resultsindicate that HOG with quadratic kernel function localises
text for shop signage better than SURF
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
This paper presents an efficient and layout-independent Automatic License
Plate Recognition (ALPR) system based on the state-of-the-art YOLO object
detector that contains a unified approach for license plate (LP) detection and
layout classification to improve the recognition results using post-processing
rules. The system is conceived by evaluating and optimizing different models,
aiming at achieving the best speed/accuracy trade-off at each stage. The
networks are trained using images from several datasets, with the addition of
various data augmentation techniques, so that they are robust under different
conditions. The proposed system achieved an average end-to-end recognition rate
of 96.9% across eight public datasets (from five different regions) used in the
experiments, outperforming both previous works and commercial systems in the
ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other
datasets, the proposed approach achieved competitive results to those attained
by the baselines. Our system also achieved impressive frames per second (FPS)
rates on a high-end GPU, being able to perform in real time even when there are
four vehicles in the scene. An additional contribution is that we manually
labeled 38,351 bounding boxes on 6,239 images from public datasets and made the
annotations publicly available to the research community