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1. 发明授权

US08913836B1 Method and system for correcting projective distortions using eigenpoints 有权
标题翻译：使用特征点校正投射失真的方法和系统
公开(公告)号：US08913836B1
公开(公告)日：2014-12-16
申请号：US14136501
申请日：2013-12-20
申请人： Jianglin Ma , Michel Dauw , Pierre De Muelenaere , Olivier Dupont
发明人： Jianglin Ma , Michel Dauw , Pierre De Muelenaere , Olivier Dupont
IPC分类号： G06K9/00 , H04N1/409
CPC分类号： H04N1/387 , G06K9/3283
摘要： Method, system, device and computer program product for projective correction of an image containing at least one text portion that is distorted by perspective. The method includes a step of image binarization involving binarizing said image. The method includes connected component analysis. Pixel blobs are detected in said at least one text portion of said binarized image in connected component analysis. The method includes horizontal vanishing point determination, including estimating text baselines by means of eigenpoints of said pixel blobs and determining a horizontal vanishing point of said at least one text portion by means of said text baselines. The method also includes vertical vanishing point determination. A vertical vanishing point is determined for said at least one text portion on the basis of vertical features thereof. The method includes projective correction. Perspective in said image is corrected on the basis of said horizontal and vertical vanishing points.
摘要翻译：方法，系统，设备和计算机程序产品，用于投影校正包含至少一个被透视变形的文本部分的图像。该方法包括涉及二值化所述图像的图像二值化步骤。该方法包括连通分量分析。在连通分量分析中，在所述二值化图像的所述至少一个文本部分中检测像素斑点。该方法包括水平消失点确定，包括通过所述像素斑点的特征点估计文本基线，并且借助于所述文本基线确定所述至少一个文本部分的水平消失点。该方法还包括垂直消失点确定。基于其垂直特征为所述至少一个文本部分确定垂直消失点。该方法包括投影校正。基于所述水平和垂直消失点来校正所述图像中的透视。

2. 发明授权

US08897600B1 Method and system for determining vanishing point candidates for projective correction 有权
标题翻译：用于确定投影校正的消失点候选的方法和系统
公开(公告)号：US08897600B1
公开(公告)日：2014-11-25
申请号：US14136585
申请日：2013-12-20
申请人： Jianglin Ma , Michel Dauw , Pierre De Muelenaere , Olivier Dupont
发明人： Jianglin Ma , Michel Dauw , Pierre De Muelenaere , Olivier Dupont
IPC分类号： G06K9/00 , G06T5/00 , G06T3/00 , G06K9/32 , G06T7/00 , G06K9/36
CPC分类号： G06T7/0061 , G06K9/3283 , G06K2009/363 , G06T3/0006 , G06T5/006 , G06T2207/10008 , G06T2207/20076 , G06T2207/30176
摘要： Method, system, device and computer program product for determining vanishing point candidates of a text portion in an image document distorted by perspective. The method includes the steps of image binarization, connected component analysis, estimating a number of text lines in a Cartesian coordinate system, transforming the text lines to data points in a homogenous coordinate system, assigning a confidence level to the data points, grouping a number of data points into a priority sample array, clustering the data points in the priority sample array into a number of sample groups and assigning a group confidence value to each sample group. A RANSAC algorithm is applied to determine among the data points a set of inliers, initiated with the sample group having the highest group confidence value. A vanishing point candidate is determined from the text lines corresponding to the set of inliers.
摘要翻译：用于确定由透视图扭曲的图像文档中的文本部分的消失点候选的方法，系统，设备和计算机程序产品。该方法包括以下步骤：图像二值化，连接分量分析，估计笛卡尔坐标系中的文本行数，将文本行变换为均匀坐标系中的数据点，将置信度等级分配给数据点，的数据点变成优先级样本阵列，将优先级样本阵列中的数据点聚类成多个样本组，并将组置信度值分配给每个样本组。应用RANSAC算法在数据点之间确定由具有最高组置信度值的样本组启动的一组内联。从对应于一组内联的文本行确定消失点候选。

3. 发明授权

US08811751B1 Method and system for correcting projective distortions with elimination steps on multiple levels 有权
标题翻译：多层次消除步骤纠正投射失真的方法和系统
公开(公告)号：US08811751B1
公开(公告)日：2014-08-19
申请号：US14136695
申请日：2013-12-20
申请人： Jianglin Ma , Michel Dauw , Pierre De Muelenaere , Olivier Dupont
发明人： Jianglin Ma , Michel Dauw , Pierre De Muelenaere , Olivier Dupont
IPC分类号： G06K9/48
CPC分类号： G06K9/3275 , G06T3/00 , G06T5/006 , G06T2207/20164
摘要： Method, system, device and computer program product for projective correction of an image containing at least one text portion that is distorted by perspective. The method includes the steps of image binarization, connected component analysis, horizontal vanishing point determination, vertical vanishing point determination and projective correction. The horizontal vanishing point is determined by estimating text baselines by means of position determining pixels of pixel blobs, identifying horizontal vanishing point candidates from the baselines, and determining a horizontal vanishing point from the candidates. The vertical vanishing point is determined on the basis of vertical features of the text portion. The method includes a first elimination step on the level of position determining pixels, a second elimination step on the level of text baselines and a third elimination step on the level of horizontal vanishing point candidates.
摘要翻译：方法，系统，设备和计算机程序产品，用于投影校正包含至少一个被透视变形的文本部分的图像。该方法包括图像二值化，连通分量分析，水平消隐点确定，垂直消失点确定和投影校正等步骤。通过位置确定像素斑点的像素，从基线识别水平消失点候选以及从候选中确定水平消失点来估计文本基线来确定水平消失点。基于文本部分的垂直特征确定垂直消失点。该方法包括在位置确定像素的级别上的第一消除步骤，文本基线的级别上的第二消除步骤和关于水平消失点候选的级别的第三消除步骤。

4. 发明授权

US09183636B1 Line segmentation method 有权
标题翻译：线分割方法
公开(公告)号：US09183636B1
公开(公告)日：2015-11-10
申请号：US14254096
申请日：2014-04-16
申请人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
发明人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
IPC分类号： G06K9/34 , G06T7/00
CPC分类号： G06T7/0079 , G06K9/344 , G06K9/348 , G06K9/68 , G06K2209/01 , G06T7/10 , G06T2207/30176
摘要： A line segmentation method which starts with determining a first starting point coordinate and generating a list of potential character widths dependent on a maximum character width stored in a database and on characteristics of the portion of the line of text corresponding to the maximum character width. The method determines a second portion of the line of text corresponding to the first starting point coordinate and the first width on the list of potential character widths. On the second portion, a classification method is applied providing a likelihood of error for the first width and a candidate character. The likelihood of error is compared with a first threshold determined by a trade-off between speed and accuracy, and if the likelihood of error corresponding to the first width is lower than the threshold value, the candidate character is selected as the character meaning that a segment is known.
摘要翻译：线分割方法，其以确定第一起点坐标并根据存储在数据库中的最大字符宽度以及对应于最大字符宽度的文本行的部分的特征来生成潜在字符宽度的列表开始。该方法确定对应于第一起点坐标的文本行的第二部分和潜在字符宽度列表上的第一宽度。在第二部分，应用分类方法提供第一宽度和候选字符的误差的可能性。将误差的可能性与通过速度和精度之间的权衡确定的第一阈值进行比较，并且如果与第一宽度相对应的误差的可能性低于阈值，则选择候选字符作为字符，这意味着a 段是已知的。

5. 发明授权

US09058517B1 Pattern recognition system and method using Gabor functions 有权
标题翻译：模式识别系统和方法使用Gabor函数
公开(公告)号：US09058517B1
公开(公告)日：2015-06-16
申请号：US14254039
申请日：2014-04-16
申请人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
发明人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
IPC分类号： G06K9/00 , G06F17/30 , G06K9/46 , G06F17/27
CPC分类号： G06K9/0053 , G06F17/27 , G06F17/2765 , G06F17/30247 , G06K9/00503 , G06K9/00523 , G06K9/00536 , G06K9/46 , G06K9/4614 , G06K9/4619 , G06K9/4623 , G06K9/6249 , G06K2209/01
摘要： A pattern recognition system and method which generates a feature vector by multiplying an image vector with a sparse matrix. The sparse matrix is generated from a Gabor function which is a sinusoidal wave multiplied by a Gaussian function. The Gabor function is a function of a set of parameters including a parameter related to the direction of the sinusoidal wave, a parameter related to a center of the Gabor function, and a parameter related to a wavelength of the sinusoidal wave. The wavelength takes at least two values, with a first wavelength value lower than or substantially equal to the distance between two adjacent centers of the Gabor function, and the first wavelength value is lower than a second wavelength value and higher than or substantially equal to half the second wavelength value.
摘要翻译：一种通过将图像向量与稀疏矩阵相乘来生成特征向量的模式识别系统和方法。稀疏矩阵由Gabor函数产生，Gabor函数是正弦波乘以高斯函数。 Gabor函数是包括与正弦波的方向有关的参数，与Gabor函数的中心相关的参数以及与正弦波的波长有关的参数的一组参数的函数。该波长具有至少两个值，第一波长值低于或基本上等于Gabor函数的两个相邻中心之间的距离，并且第一波长值低于第二波长值并且高于或基本上等于一半第二波长值。

6. 发明申请

US20150302598A1 LINE SEGMENTATION METHOD 有权
标题翻译：线分割方法
公开(公告)号：US20150302598A1
公开(公告)日：2015-10-22
申请号：US14254096
申请日：2014-04-16
申请人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
发明人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
IPC分类号： G06T7/00
CPC分类号： G06T7/0079 , G06K9/344 , G06K9/348 , G06K9/68 , G06K2209/01 , G06T7/10 , G06T2207/30176
摘要： A line segmentation method which starts with determining a first starting point coordinate and generating a list of potential character widths dependent on a maximum character width stored in a database and on characteristics of the portion of the line of text corresponding to the maximum character width. The method determines a second portion of the line of text corresponding to the first starting point coordinate and the first width on the list of potential character widths. On the second portion, a classification method is applied providing a likelihood of error for the first width and a candidate character. The likelihood of error is compared with a first threshold determined by a trade-off between speed and accuracy, and if the likelihood of error corresponding to the first width is lower than the threshold value, the candidate character is selected as the character meaning that a segment is known.
摘要翻译：线分割方法，其以确定第一起点坐标并根据存储在数据库中的最大字符宽度以及对应于最大字符宽度的文本行的部分的特征来生成潜在字符宽度的列表开始。该方法确定对应于第一起点坐标的文本行的第二部分和潜在字符宽度列表上的第一宽度。在第二部分，应用分类方法提供第一宽度和候选字符的误差的可能性。将误差的可能性与通过速度和精度之间的权衡确定的第一阈值进行比较，并且如果与第一宽度相对应的误差的可能性低于阈值，则选择候选字符作为字符，这意味着a 段是已知的。

7. 发明授权

US08699100B2 Autonomous sheet-fed scanner 有权
标题翻译：自动单张纸扫描仪
公开(公告)号：US08699100B2
公开(公告)日：2014-04-15
申请号：US13023666
申请日：2011-02-09
申请人： Pierre De Muelenaere , Michel Dauw , Olivier Dupont , Patrick Verleysen
发明人： Pierre De Muelenaere , Michel Dauw , Olivier Dupont , Patrick Verleysen
IPC分类号： H04N1/46 , B65H33/04 , H04N1/04
CPC分类号： H04N1/00222 , H04N2201/0044 , H04N2201/0081 , H04N2201/0096
摘要： Battery-powered portable sheet-fed scanner, comprising: a scanning unit (21-23) for scanning documents and forming digital representations thereof; a sheet feeder (28) for feeding a document past the scanning unit; a processor (20), communicatively connected to the scanning unit and provided for controlling the scanning operation; a plurality of embedded storage capabilities, each of which is communicatively connected to the processor and each of which comprises either an internal memory (26) for internally storing the digital representations or a communication link (24; 25) to an external storage medium for externally storing the digital representations. The processor is provided with an embedded routing application which is provided for routing the digital representations to a predetermined selection among the embedded storage capabilities.
摘要翻译：电池供电的便携式单张纸扫描器，包括：扫描单元（21-23），用于扫描文件并形成数字表示; 用于将文件馈送通过扫描单元的送纸器（28）处理器（20），通信地连接到扫描单元并被提供用于控制扫描操作; 多个嵌入式存储能力，其中每一个通信地连接到处理器，其中每一个包括用于将数字表示内部存储的内部存储器（26）或用于外部的外部存储介质的通信链路（24; 25）存储数字表示。该处理器设置有嵌入式路由应用，其被提供用于将数字表示路由到嵌入式存储能力中的预定选择。

8. 发明授权

US08463054B2 Hierarchical OCR using decision tree and nonparametric classifier 有权
标题翻译：分层OCR使用决策树和非参数分类器
公开(公告)号：US08463054B2
公开(公告)日：2013-06-11
申请号：US13442192
申请日：2012-04-09
申请人： Pierre De Muelenaere , Michel Dauw , Olivier Dupont , Patrick Verleysen
发明人： Pierre De Muelenaere , Michel Dauw , Olivier Dupont , Patrick Verleysen
IPC分类号： G06K9/00
CPC分类号： G06K9/44 , G06K9/6282 , G06K2209/01
摘要： A pattern recognition process, a non-transitory computer program product, and a mobile terminal for pattern recognition. An input pattern is normalized and a reliable pattern is generated from the normalized pattern by using at least one morphological operator. The distance between the reliable pattern and selected templates which are selected from a template library using a decision tree, is calculated. The reliable patterns are classified into at least one of the classes of the selected templates by at least one non-parametric classification method.
摘要翻译：模式识别过程，非暂时性计算机程序产品和用于模式识别的移动终端。通过使用至少一个形态运算符将输入图案归一化并从标准化图案生成可靠图案。计算从使用决策树的模板库中选择的可靠模式与所选模板之间的距离。通过至少一种非参数分类方法将可靠模式分类为所选模板的类别中的至少一个。

9. 发明授权

US09311558B2 Pattern recognition system 有权
标题翻译：模式识别系统
公开(公告)号：US09311558B2
公开(公告)日：2016-04-12
申请号：US14254076
申请日：2014-04-16
申请人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
发明人： Frederic Collet , Jordi Hautot , Michel Dauw , Pierre De Muelenaere , Olivier Dupont , Gunter Hensges
IPC分类号： G06K9/62 , G06K9/46 , G06T5/10
CPC分类号： G06K9/4604 , G06K9/4619 , G06K9/6249 , G06K9/6267 , G06K2209/01 , G06T5/10
摘要： A method for identifying a pattern in an image. In a first step the image is normalized to a binary matrix. A binary vector is subsequently generated from the binary matrix. The binary vector is filtered with a sparse matrix to a feature vector using a matrix vector multiplication wherein the matrix vector multiplication determines the values of the feature vector by applying program steps which are the result of transforming the sparse matrix in program steps including conditions on the values of the binary vector. Lastly, from the feature vector, a density of probability for a predetermined list of models is generated to identify the pattern in the image.
摘要翻译：用于识别图像中的图案的方法。在第一步中，图像被归一化为二进制矩阵。随后从二进制矩阵生成二进制向量。使用矩阵向量乘法将二进制向量用稀疏矩阵过滤到特征向量，其中矩阵向量乘法通过应用程序步骤来确定特征向量的值，该程序步骤是在程序步骤中变换稀疏矩阵的结果，包括在二进制向量的值。最后，从特征向量，生成用于预定列表模型的概率密度，以识别图像中的图案。

10. 发明申请

US20130039591A1 PATTERN RECOGNITION PROCESS, COMPUTER PROGRAM PRODUCT AND MOBILE TERMINAL 有权
标题翻译：模式识别过程，计算机程序产品和移动终端
公开(公告)号：US20130039591A1
公开(公告)日：2013-02-14
申请号：US13442192
申请日：2012-04-09
申请人： Pierre De Muelenaere , Michel Dauw , Olivier Dupont , Patrick Verleysen
发明人： Pierre De Muelenaere , Michel Dauw , Olivier Dupont , Patrick Verleysen
IPC分类号： G06K9/62
CPC分类号： G06K9/44 , G06K9/6282 , G06K2209/01
摘要： Pattern recognition process that includes, starting from an input pattern: normalization of the input pattern into a normalized pattern of predetermined size; generation of a reliable pattern from the normalized pattern by using at least one morphological operator; calculation of a distance between the reliable pattern and selected templates which are selected from a template library; classification of the reliable patterns into at least one of the classes of the selected templates by means of at least one non-parametric classification method, which uses the classes of the selected templates and the calculated distances as inputs and outputs identified classes along with confidence levels.
摘要翻译：模式识别处理包括从输入图案开始：将输入图案归一化为规定尺寸的规格化图案; 通过使用至少一个形态算子从标准化图案生成可靠图案; 计算从模板库中选择的可靠模式与所选模板之间的距离; 通过至少一个非参数分类方法将可靠模式分类为所选模板的类别中的至少一个，其使用所选模板的类别和计算出的距离作为输入，并且输出所识别的类以及置信水平。

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