Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed.  divided the on-line graphics recognition process into four stages: preprocessing, shape classification, shape fitting, and regularization. The attraction force model is proposed to combine progressively the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined After that, the shape is fitted and gradually rectified to a regular one, thus the regularized shape fits the user-intended one precisely. Experimental results show that this algorithm can rapidly yield good recognition precision (averagely above 90%) and a fine regularization effect. Consequently, it is especially suitable for ak computation environments such as PDAs, which solely depend on a pen-based user interface.
['Jin Xiangyu', 'Liu Wenyin', 'Sun Jian-yong', 'Zhengxing Sun']
I am writing a research paper on a idea On-line graphics recognition
[{"from": "human", "value": "I am writing a research paper on a idea On-line graphics recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed.  divided the on-line graphics recognition process into four stages: preprocessing, shape classification, shape fitting, and regularization. The attraction force model is proposed to combine progressively the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined After that, the shape is fitted and gradually rectified to a regular one, thus the regularized shape fits the user-intended one precisely. Experimental results show that this algorithm can rapidly yield good recognition precision (averagely above 90%) and a fine regularization effect. Consequently, it is especially suitable for ak computation environments such as PDAs, which solely depend on a pen-based user interface."}]