公开(公告)号：US20160364633A1

公开(公告)日：2016-12-15

申请号：US14734466

申请日：2015-06-09

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： JIANCHAO YANG ,  ZHANGYANG WANG ,  JONATHAN BRANDT ,  HAILIN JIN ,  ELYA SHECHTMAN ,  ASEEM OMPRAKASH AGARWALA

IPC: G06K9/66 ,  G06K9/68 ,  G06T3/40 ,  G06K9/62

CPC classification number: G06T3/40 ,  G06K9/6255 ,  G06K9/6828

Abstract: A convolutional neural network (CNN) is trained for font recognition and font similarity learning. In a training phase, text images with font labels are synthesized by introducing variances to minimize the gap between the training images and real-world text images. Training images are generated and input into the CNN. The output is fed into an N-way softmax function dependent on the number of fonts the CNN is being trained on, producing a distribution of classified text images over N class labels. In a testing phase, each test image is normalized in height and squeezed in aspect ratio resulting in a plurality of test patches. The CNN averages the probabilities of each test patch belonging to a set of fonts to obtain a classification. Feature representations may be extracted and utilized to define font similarity between fonts, which may be utilized in font suggestion, font browsing, or font recognition applications.

Abstract translation: 对卷积神经网络（CNN）进行字体识别和字体相似学习。 在训练阶段，通过引入差异来合成具有字体标签的文本图像，以最小化训练图像与真实世界文本图像之间的差距。 生成训练图像并将其输入到CNN中。 根据CNN正在训练的字体数量，输出被输入到N-way softmax函数中，产生N类标签上分类文本图像的分布。 在测试阶段，每个测试图像的高度被标准化，并以纵横比挤压，从而产生多个测试贴片。 CNN对属于一组字体的每个测试补丁的概率进行平均，以获得分类。 可以提取和利用特征表示来定义可以在字体建议，字体浏览或字体识别应用中使用的字体之间的字体相似性。

公开(公告)号：US20170061257A1

公开(公告)日：2017-03-02

申请号：US15349876

申请日：2016-11-11

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： JIANCHAO YANG ,  GUANG CHEN ,  HAILIN JIN ,  JONATHAN BRANDT ,  ELYA SHECHTMAN ,  ASEEM OMPRAKASH AGARWALA

IPC: G06K9/62 ,  G06K9/48

CPC classification number: G06K9/6282 ,  G06K9/6219 ,  G06K9/6267 ,  G06K9/6807

Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.

Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。