A brief description of ideas: 1, image binarization Pictures of all the points expressed by 0 or 1, 1 for the effective point, 0 for background. Used here is the largest between-class variance (Otsu), are introduced in the data. 2, removal of interference points 3, partition The whole picture is divided into each individual character, in the next step in order to identify 11. 4, compared with the sample database, the search might match the recent This step is to compare the core places, due to the recognition of each of the graphics changes are random, we can not be finished Identification of the entire match, so the use of the Euclidean distance to be like the recent match, the information in the "free handwritten Digital Identification, "which is described in detail. (Sample library features in accordance with the matching pre-prepared through the process of learning to be) The identification of a lot of ide

识别程序
........\code.lib
........\comm.pas
........\func.pas
........\img





........\kind1.pas
........\NdOcr.pas
........\Project1.cfg
........\Project1.dof
........\Project1.dpr
........\Project1.exe
........\Project1.res
........\Unit1.ddp
........\Unit1.dfm
........\Unit1.pas
资料
....\bmp文件格式.pdf
....\otsu.files





....\..........\60-2.jpg
....\..........\60-3.jpg
....\..........\60-4.jpg
....\..........\60-5.jpg
....\..........\60-6.jpg
....\..........\60-7.jpg
....\..........\60-8.jpg
....\..........\60-9.jpg
....\..........\61-1.jpg
....\..........\61-2.jpg
....\..........\top1.gif
....\..........\top2.gif
....\..........\top3.gif
....\..........\top4.gif
....\..........\top5.gif
....\..........\top6.gif
....\最大类间方差法-otsu.htm
....\自由手写体数字识别.doc

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