Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9) serve as the first stage classifier. In the second stage, a support vector machine (SVM) trained on scale-invariant feature transform (SIFT) descriptors obtained from training sub-windows re employed. A recall rate of 0.920792 and precision rate of 0.90185 was obtained.
['Wing Teng Ho', 'Hao Wooi Lim', 'Yong Haur Tay']
I am writing a research paper on a idea Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM
[{"from": "human", "value": "I am writing a research paper on a idea Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9) serve as the first stage classifier. In the second stage, a support vector machine (SVM) trained on scale-invariant feature transform (SIFT) descriptors obtained from training sub-windows re employed. A recall rate of 0.920792 and precision rate of 0.90185 was obtained."}]