Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: License-plate recognition plays an important role in numerous applications, and a number of techniques have been proposed. , a novel method to recognize license plates is presented. First, the license plates are located using salient features. Then, each of the seven characters in a license plate is segmented. Finally, the character recognizer extracts some salient features of the characters and uses a feature-salience classifier to achieve robust recognition results. In the experiments, 1176 images that re taken from various scenes and conditions re used, and 32 images out of the 1176 images failed to correctly locate the license plates, which amounts to a success rate of 97.3%. In the experiments on identifying license characters,  used 1144 images, for which license plates have successfully been located and out of which 49 images failed to identify the characters; the rate of successful identification is 95.7%. Combining the preceding two rates, the overall rate of success of the developed method is 93.1%.
['Zhenxue Chen', 'Chengyun Liu', 'Faliang Chang', 'Guoyou Wang']
I am writing a research paper on a idea Automatic License-Plate Location and Recognition Based on Feature Salience
[{"from": "human", "value": "I am writing a research paper on a idea Automatic License-Plate Location and Recognition Based on Feature Salience"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: License-plate recognition plays an important role in numerous applications, and a number of techniques have been proposed. , a novel method to recognize license plates is presented. First, the license plates are located using salient features. Then, each of the seven characters in a license plate is segmented. Finally, the character recognizer extracts some salient features of the characters and uses a feature-salience classifier to achieve robust recognition results. In the experiments, 1176 images that re taken from various scenes and conditions re used, and 32 images out of the 1176 images failed to correctly locate the license plates, which amounts to a success rate of 97.3%. In the experiments on identifying license characters,  used 1144 images, for which license plates have successfully been located and out of which 49 images failed to identify the characters; the rate of successful identification is 95.7%. Combining the preceding two rates, the overall rate of success of the developed method is 93.1%."}]