公开(公告)号：US09576210B1

公开(公告)日：2017-02-21

申请号：US14500005

申请日：2014-09-29

Applicant: Amazon Technologies, Inc.

Inventor： Yue Liu ,  Qingfeng Yu ,  Xing Liu ,  Pradeep Natarajan

IPC: G06K9/18 ,  G06K9/46 ,  G06T5/00 ,  G06T11/60 ,  G06T5/10

CPC classification number: G06T5/10 ,  G06K9/22 ,  G06K9/4604 ,  G06K2209/01 ,  G06T5/003 ,  G06T7/0002 ,  G06T11/60 ,  G06T2207/10016 ,  G06T2207/20192 ,  G06T2207/30168

Abstract: A system to select video frames for optical character recognition (OCR) based on feature metrics associated with blur and sharpness. A device captures a video frame including text characters. An edge detection filter is applied to the frame to determine gradient features in perpendicular directions. An “edge map” is created from the gradient features, and points along edges in the edge map are identified. Edge transition widths are determined at each of the edge points based in local intensity minimum and maximum on opposite sides of the respective edge point in the frame. Sharper edges have smaller edge transition widths than blurry images. Statistics are determined from the edge transition widths, and the statistics are processed by a trained classifier to determine if the frame is or is not sufficiently sharp for text processing.

Abstract translation: 基于与模糊和锐度相关联的特征度量来选择用于光学字符识别（OCR）的视频帧的系统。 设备捕获包含文本字符的视频帧。 将边缘检测滤波器应用于帧以确定垂直方向上的梯度特征。 从梯度特征创建“边缘图”，并且识别沿着边缘图中边缘的点。 基于帧中相应边缘点的相对侧上的局部强度最小值和最大值，在每个边缘点处确定边缘过渡宽度。 更亮的边缘具有比模糊图像更小的边缘过渡宽度。 根据边缘转换宽度确定统计量，并且由训练有素的分类器处理统计信息，以确定帧是否为文本处理不够清晰。

公开(公告)号：US09418316B1

公开(公告)日：2016-08-16

申请号：US14500208

申请日：2014-09-29

Applicant: Amazon Technologies, Inc.

Inventor： Yue Liu ,  Qingfeng Yu ,  Xing Liu ,  Pradeep Natarajan

IPC: G06K9/62 ,  G06K9/66 ,  G06K9/18

CPC classification number: G06K9/3258 ,  G06K9/6231 ,  G06K2209/01

Abstract: A process for training and optimizing a system to select video frames for optical character recognition (OCR) based on feature metrics associated with blur and sharpness. A set of image frames are subjectively labelled based on a comparison of each frame before and after binarization to determine to what degree text is recognizable in the binary image. A plurality of different sharpness feature metrics are generated based on the original frame. A classifier is then trained using the feature metrics and the subjective labels. The feature metrics are then tested for accuracy and/or correlation with subjective labelling data. The set of feature metrics may be refined based on which metrics produce the best results.

Abstract translation: 基于与模糊和锐度相关的特征量度，训练和优化系统以选择用于光学字符识别（OCR）的视频帧的过程。 基于二值化之前和之后的每个帧的比较来主观地标记一组图像帧，以确定二进制图像中文本是可识别的。 基于原始帧生成多个不同的锐度特征度量。 然后使用特征指标和主观标签对分类器进行训练。 然后测试特征度量的准确性和/或与主观标记数据的相关性。 可以基于哪些度量产生最佳结果来改进特征度量集合。