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    • 2. 发明公开
    • OPTICAL CHARACTER RECOGNITION LOCALIZATION TOOL
    • LOKALISIERUNGSWERKZEUG ZUR ERKENNUNG OPTISCHER ZEICHEN
    • EP3125159A1
    • 2017-02-01
    • EP16181289.6
    • 2016-07-26
    • Datalogic IP TECH S.r.l.
    • DEPPIERI, FrancescoGAMBINI, Andrea
    • G06K9/34
    • G06K9/344G06K9/18G06K9/34G06K9/348G06K9/72
    • Systems and methods for processing images to recognize characters. Such may include locating background in an image which separate lines of characters, and then locating background which separate characters. The inter-character spaces within a line may be used to determine a probable inter-character spacing for the characters. Within each detected line, the character having an inter-character spacing most similar to the probable inter-character spacing may be set as a starting character for classification. Using the probable inter-character spacing and the location of the starting character, the location of a character adjacent to the starting character in a first direction may be determined and the character classified. Such process may repeat in the first direction until a first end of the line is reached. The process may then move to a character adjacent the starting character in a second direction and repeat until a second end of the line is reached.
    • 用于处理图像以识别字符的系统和方法。 这样可以包括将图像中的背景定位成单独的字符行,然后定位分离字符的背景。 行中的字符间空格可用于确定字符的可能的字符间距。 在每个检测到的行中,具有与可能的字符间间隔最相似的字符间间隔的字符可以被设置为用于分类的起始字符。 使用可能的字符间距和起始字符的位置,可以确定与第一方向上的起始字符相邻的字符的位置,并且字符被分类。 该过程可以在第一方向上重复,直到达到线的第一端。 然后,该过程可以在第二方向上移动到与起始字符相邻的字符,并重复直到到达行的第二端。
    • 3. 发明公开
    • METHOD OF DECODING OPTICAL INFORMATION
    • VERFAHREN ZUR DECODIERUNG OPTISCHER INFORMATIONEN
    • EP2795534A1
    • 2014-10-29
    • EP11815796.5
    • 2011-12-22
    • Datalogic IP TECH S.r.l.
    • ZOCCA, RinaldoDEPPIERI, FrancescoPASCARELLA, Antonio
    • G06K7/14G06K9/03G06K9/62G05B13/02G06K7/10
    • G06K7/1404G06K7/10544G06K9/036G06K9/6262
    • A method for decoding optical information present in an acquired image comprises identifying a set of functional parameters that are settable for processing the image and a plurality of instances of said set of functional parameters. A different mode for processing this image corresponds to each of these instances. The method comprises selecting a determined processing mode for decoding the optical information from the processing modes of a working set of processing modes, and applying this selected processing mode and further comprises associating with each processing mode a respective probability of success. Selecting one of the methods of the working set comprises choosing the processing mode with the highest value of probability of success and updating after each application of the selected processing mode, both if said decoding was successful and if said decoding was not successful, the probability of success of each processing mode of the working set, such as to perform adaptive decoding of the optical information.
    • 一种方法包括识别可设置用于处理图像的一组功能参数以及该组功能参数的多个实例。 处理此图像的不同模式对应于这些实例中的每一个。 该方法包括从处理模式的工作集合的处理模式中选择用于对光学信息进行解码的确定处理模式,以及应用该选择的处理模式,并且进一步包括与每个处理模式相关联的成功概率。 选择工作集的方法之一包括在每个应用所选处理模式之后选择具有成功和更新概率的最高值的处理模式,如果解码成功,并且如果解码不成功,则概率 工作组的每个处理模式的成功,如执行光信息的自适应解码。
    • 5. 发明公开
    • SYSTEMS, METHODS AND ARTICLES FOR READING HIGHLY BLURRED MACHINE-READABLE SYMBOLS
    • SYSTEME,VERFAHREN UND ARTIKEL ZUM LESEN HOCHVERSCHWOMMENER MASCHINENLESBARER SYMBOLE
    • EP3016028A1
    • 2016-05-04
    • EP15192046.9
    • 2015-10-29
    • Datalogic IP TECH S.r.l.
    • DEPPIERI, FrancescoDE GIROLAMI, Maurizio AldoLANZA, AlessandroSGALLARI, Fiorella
    • G06K9/18G06K9/52
    • G06K7/1473G06K9/183G06K9/522
    • Systems and methods for robust recognition of machine-readable symbols from highly blurred or distorted images. An image signal representation of a machine-readable symbol element is transformed into a different space using one or more transform operations, which moves an n-dimensional vector of measured light intensities into another n-dimensional space. The types of transform operations may include blur robust orthonormal bases, such as the Discrete Sine Transform, the Discrete Cosine Transform, the Chebyshev Transform, and the Lagrange Transform. A trained classifier (e.g., an artificial intelligence machine learning algorithm) may be used to classify the transformed signal in the transformed space. The types of trainable classifiers that may be used include random forest classifiers, Mahalanobis classifiers, support vector machines, and classification or regression trees.
    • 用于从高度模糊或失真的图像鲁棒识别机器可读符号的系统和方法。 使用一个或多个变换操作将机器可读符号元素的图像信号表示变换成不同的空间,其将测量的光强度的n维向量移动到另一个n维空间中。 变换操作的类型可以包括模糊鲁棒的正交基,例如离散正弦变换,离散余弦变换,切比雪夫变换和拉格朗日变换。 可以使用经过训练的分类器(例如,人造智能机器学习算法)来对经变换的空间中的变换后的信号进行分类。 可以使用的可训练分类器的类型包括随机森林分类器,马哈拉诺比斯分类器,支持向量机和分类或回归树。