Impact of Font on Computer Recognition of License Plates on Automobiles

Al-qudah, Rabiah; Suen, Ching Y.

doi:10.1145/3387168.3389112

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2022

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“…Although recognizing text in natural scenes has been an ongoing research topic for decades, many studies focused on the consistent appearance of text like in traffic signs [3], and license plates [4]. However, other signs like shop signboards have gotten less attention because of their complex visual appearance.…”

Section: Introductionmentioning

confidence: 99%

AI Based Approach for Shop Classification and a Comparative Study with Human

Almuhajri¹,

Suen²

2022

AAIML

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The rapid advancements in artificial intelligence algorithms have sharpened the focus on street signs due to their prevalence. Some street signs have consistent shapes and pre-defined colors and fonts, such as traffic signs while others are characterized by their visual variability like shop signboards. This variations create a complicated challenge for AI-based systems to classify them. In this paper, the annotation of the ShoS dataset were extended to include more attributes for shop classification. Then, two classifiers were trained and tested utilizing the extended ShoS dataset. SVM showed great performance as its F1-score reached 89.33\%. The classification performance was compared with human performance, and the results showed that our classifier excelled over human performance by about 15\%. The results were discussed, so the factors that affect classification were provided for further enhancement.

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Section: Introductionmentioning

confidence: 99%