ALPR (Automatic License Plate Recognition) systems often function as the core module of “intelligent” transportation infrastructure applications. License plate recognition can be employed to identify a vehicle by automatically reading a license plate via image-processing and character recognition technology. A license plate recognition operation can be performed by locating the license plate in an image, segmenting the characters in the license plate, and performing an OCR (Optical Character Recognition) operation with respect to the characters identified. In order for OCR to achieve a high accuracy, it is necessary to obtain properly segmented characters.
Several approaches have been implemented for performing character segmentation on license plate images. One approach involves the use of a vertical projection histogram to produce character boundaries (cuts) and local statistical information, such as a median character spacing, to split a large cut (caused by combining characters) and to insert a missing character. Such operations require minimal computational resources and consequently applied to each input image to achieve good character segmentation accuracy. Also, such an approach utilizes a priori image information, thereby enabling robust performance over a variety of state logos, fonts, and character spacing.
A problem associated with such projective segmentation techniques is substantial variation across the plate in the regions surrounding the characters. For example, consider the cropped license plate image 100 depicted in FIG. 1. A partial obstruction 105 near the center of the plate 100 clearly represents a different local challenge as compared to other inter-character regions on the plate 100. FIG. 2 represents a license plate image 130 having a complex background pictorial 135. The projective segmentation approach failed to identify the segmentation boundaries between the pictorial 135 and the characters 140 on either side.
The complex background pictorial 135 also presents a local variation and cannot be easily overcome with a fixed segmentation threshold. Adjusting the aggressiveness of the threshold for the projective segmentation approach can help to prevent the missed cuts. Such approach often, however, leads to over-segmentation of images, as depicted in FIG. 3. Hence, it is often extremely difficult to determine the right threshold setting in order to reduce the under-segmentation event (missed cuts) without inducing unwanted over-segmentation event (split characters) across a large number of license plate images.
Based on the foregoing, it is believed that a need exists for an improved character segmentation method and system for a license plate image utilizing a reinforcement learning approach, as will be described in greater detail herein.