公开(公告)号：US20150125041A1

公开(公告)日：2015-05-07

申请号：US14159590

申请日：2014-01-21

Applicant: XEROX CORPORATION

Inventor： Aaron Michael Burry ,  Farnaz Abtahi

IPC: G06K9/32 ,  G06K9/62

CPC classification number: G06K9/325 ,  G06K9/00791 ,  G06K9/34 ,  G06K9/6256 ,  G06K2209/15

Abstract: Methods and systems for achieving accurate segmentation of characters with respect to a license plate image utilizing a reinforcement learning approach. A vehicle image can be captured by an image capturing unit and processed utilizing an ALPR (Automatic License Plate Recognition) unit. The reinforcement learning (RL) approach can be configured to initialize a segmentation agent with a starting location. A proper segmentation path (cuts) from top to bottom and from a darker to lighter area in a cropped license plate image can be identified by the segmentation agent during a training phase. Rewards can be provided based on a number of good and bad moves. The association between a current state and a sensory input with a preferred action can be learned by the segmentation agent at the end of the training phase.

Abstract translation: 使用强化学习方法实现与车牌图像相关的字符的精确分割的方法和系统。 车辆图像可以由图像捕获单元捕获并且使用ALPR（自动车牌识别）单元进行处理。 强化学习（RL）方法可以配置为初始化具有起始位置的分段代理。 可以在训练阶段通过分割代理来识别经裁剪的车牌图像中从顶部到底部以及从较暗到较亮区域的适当分割路径（切割）。 奖励可以根据一些好的和坏的举动提供。 当前状态与具有优选动作的感觉输入之间的关联可以在训练阶段结束时被分段代理学习。

公开(公告)号：US09213910B2

公开(公告)日：2015-12-15

申请号：US14159590

申请日：2014-01-21

Applicant: Xerox Corporation

Inventor： Aaron Michael Burry ,  Farnaz Abtahi

IPC: G06K9/32 ,  G06K9/62

CPC classification number: G06K9/325 ,  G06K9/00791 ,  G06K9/34 ,  G06K9/6256 ,  G06K2209/15

Abstract: Methods and systems for achieving accurate segmentation of characters with respect to a license plate image utilizing a reinforcement learning approach. A vehicle image can be captured by an image capturing unit and processed utilizing an ALPR (Automatic License Plate Recognition) unit. The reinforcement learning (RL) approach can be configured to initialize a segmentation agent with a starting location. A proper segmentation path (cuts) from top to bottom and from a darker to lighter area in a cropped license plate image can be identified by the segmentation agent during a training phase. Rewards can be provided based on a number of good and bad moves. The association between a current state and a sensory input with a preferred action can be learned by the segmentation agent at the end of the training phase.

Abstract translation: 使用强化学习方法实现与车牌图像相关的字符的精确分割的方法和系统。 车辆图像可以由图像捕获单元捕获并且使用ALPR（自动车牌识别）单元进行处理。 强化学习（RL）方法可以配置为初始化具有起始位置的分段代理。 可以在训练阶段通过分割代理来识别裁剪后的车牌图像中从顶部到底部以及从较暗到更轻的区域的适当分割路径（切割）。 奖励可以根据一些好的和坏的举动提供。 当前状态与具有优选动作的感觉输入之间的关联可以在训练阶段结束时被分段代理学习。