High accuracy optical character recognition using neural networks with
centroid dithering
    2.
    发明授权
    High accuracy optical character recognition using neural networks with centroid dithering 失效
    使用具有重心抖动的神经网络的高精度光学字符识别

    公开(公告)号:US5475768A

    公开(公告)日:1995-12-12

    申请号:US55523

    申请日:1993-04-29

    CPC分类号: G06K9/6217 G06K9/36

    摘要: Pattern recognition, for instance optical character recognition, is achieved by training a neural network, scanning an image, segmenting the image to detect a pattern, preprocessing the detected pattern, and applying the preprocessed detected pattern to the trained neural network. The preprocessing includes determining a centroid of the pattern and centrally positioning the centroid in a frame containing the pattern. The training of the neural network includes randomly displacing template patterns within frames before applying the template patterns to the neural network.

    摘要翻译: 模式识别,例如光学字符识别,通过训练神经网络,扫描图像,分割图像以检测图案,预处理检测到的图案,以及将经过预处理的检测图案应用于经过训练的神经网络来实现。 预处理包括确定图案的质心并且在包含图案的框中心定位质心。 神经网络的训练包括在将模板图案应用于神经网络之前随机移位框架内的模板模式。

    Training a neural network using centroid dithering by randomly
displacing a template
    3.
    发明授权
    Training a neural network using centroid dithering by randomly displacing a template 失效
    通过随机取代模板来训练使用质心抖动的神经网络

    公开(公告)号:US5625707A

    公开(公告)日:1997-04-29

    申请号:US445470

    申请日:1995-05-22

    CPC分类号: G06K9/6217 G06K9/36

    摘要: Pattern recognition, for instance optical character recognition, is achieved by training a neural network, scanning an image, segmenting the image to detect a pattern, preprocessing the detected pattern, and applying the preprocessed detected pattern to the trained neural network. The preprocessing includes determining a centroid of the pattern and centrally positioning the centroid in a frame containing the pattern. The training of the neural network includes randomly displacing template patterns within frames before applying the template patterns to the neural network.

    摘要翻译: 模式识别,例如光学字符识别,通过训练神经网络,扫描图像,分割图像以检测图案,预处理检测到的图案,以及将经过预处理的检测图案应用于经过训练的神经网络来实现。 预处理包括确定图案的质心并且在包含图案的框中心定位质心。 神经网络的训练包括在将模板图案应用于神经网络之前随机移位框架内的模板模式。

    Adaptive non-literal text string retrieval
    4.
    发明授权
    Adaptive non-literal text string retrieval 失效
    自适应非文字文本字符串检索

    公开(公告)号:US5600835A

    公开(公告)日:1997-02-04

    申请号:US561204

    申请日:1995-11-20

    IPC分类号: G06F17/27 G06F17/30 G06F17/28

    摘要: Method and system for selectively retrieving information contained in a stored document set using a non-literal, or "fuzzy", search strategy. A text string query is transmitted (200) to a computer processor, and a dissimilarity value D.sub.i is assigned (208) to selected ones of stored text strings representative of information contained in a stored document set, based upon a first set of rules (106). A set of retrieved text strings representative of stored information and related to the text string query is generated (212), based upon a second set of rules (107). Each of the retrieved text strings has an associated dissimilarity value D.sub.i, which is a function of at least one rule R.sub.n from the first set of rules (106) used to retrieve the text string and a weight value w.sub.n associated with that rule R.sub.n. The retrieved text strings are displayed (216) preferably in an order based on their associated dissimilarity value D.sub.i. Once one or more of the retrieved text strings is chosen, the weight value w.sub.n associated with at least one rule of the first set of rules (106) is adjusted (220) and stored.

    摘要翻译: 用于使用非文字或“模糊”搜索策略选择性地检索包含在存储的文档集中的信息的方法和系统。 将文本串查询(200)发送到计算机处理器,并且基于第一组规则(106)将不同值Di分配(208)到代表存储的文档集中包含的信息的存储的文本串中的所选择的文本串 )。 基于第二组规则(107)生成代表存储的信息并与文本串查询相关的一组检索的文本串(212)。 每个检索到的文本串具有相关联的不相似性值Di,其是来自用于检索文本串的第一组规则(106)和与该规则Rn相关联的加权值wn的至少一个规则Rn的函数。 优选地,基于它们相关联的不相似性值Di以顺序显示所检索的文本串(216)。 一旦选择了一个或多个检索到的文本串,则调整(220)并存储与第一组规则(106)中的至少一个规则相关联的权重值wn。