# US 11,443,535 B2 · License plate identification method and system thereof > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,535 B2 - Document ID: US11443535B2 - Status: Granted - Kind: B2 - Application number: 16/980,747 - Filed: 2019-01-21 - Published: 2021-01-07 - Granted: 2022-09-13 - Earliest claimed: 2018-03-14 - Assignees: Delta Electronics, Inc. - Applicants: Delta Electronics, Inc. - Inventors: Yu-Ta Chen; Feng-Ming Liang; Jing-Hong Jheng - Web page: https://patentgrep.com/patent/US11443535B2 ## CPC - G06V 10/267: {by performing operations on regions, e.g. growing, shrinking or watersheds} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/22: Matching criteria, e.g. proximity measures - G06T 7/194: involving foreground-background segmentation - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V 10/758: {Involving statistics of pixels or of feature values, e.g. histogram matching} - G06V 10/82: using neural networks - G06V 20/63: {Scene text, e.g. street names} - G06V 30/153: {using recognition of characters or words} - G06V 30/1916: {Validation; Performance evaluation} - G06T 2207/30252: Vehicle exterior; Vicinity of vehicle - G06V 20/625: {License plates} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A license plate identification method is provided, including steps of: obtaining a to-be-processed image including all characters on a license plate; extracting several feature maps corresponding to character features of the to-be-processed image through a feature map extraction module; for each of the characters, extracting a block and a coordinate according to the feature maps through a character identification model based on a neural network; and obtaining a license plate identification result according to the respective blocks and the respective coordinates of the characters. ## Claims ### Claim 1 A license plate identification method, comprising steps of: receiving a raw image; extracting a historical background image through a foreground and background subtraction module; comparing the raw image with the historical background image to determine an amount of image change; and determining whether the amount of image change is greater than a predetermined value, when the amount of image change is greater than the predetermined value, generating a to-be-processed image comprising all of characters on a license plate; extracting a plurality of feature maps comprising character features of the to-be-processed image through a feature map extraction module; for each of the characters, extracting a block and a coordinate according to the feature maps through a character identification model based on a neural network; and obtaining a license plate identification result according to the respective blocks and the respective coordinates of the characters. ### Claim 2 (depends on 1) The license plate identification method as claimed in claim 1, further comprising steps of: obtaining a vehicle front image or a vehicle rear image from the raw image through a vehicle front image capturing module or a vehicle rear image capturing module by using a first image feature and a first classifier; and obtaining the to-be-processed image comprising all of the characters according to the vehicle front image or the vehicle rear image through a license plate character region detection model. ### Claim 3 (depends on 2) The license plate identification method as claimed in claim 2, further comprising steps of: obtaining at least one character block from the vehicle front image or the vehicle rear image through the license plate character region detection model by using a second image feature and a second classifier; and determining a magnification according to the number of character blocks, wherein the to-be-processed image is obtained based on the character block according to the magnification. ### Claim 4 (depends on 1) The license plate identification method as claimed in claim 1, further comprising steps of: receiving license plate identification results; dividing the plurality of license plate identification results into at least two groups according to a license plate grouping rule; voting for each sub-identification result in each of the groups; when, for each group, there is one sub-identification result having a voting score that is higher than a threshold value, generating a final license plate identification result according the sub-identification results; and updating the character identification model according to the license plate identification result or the final license plate identification result, wherein the license plate grouping rule comprises a license plate naming grouping rule, an English character region and number character region grouping rule, a dash grouping rule, and a character relative position grouping rule. ### Claim 5 (depends on 4) The license plate identification method as claimed in claim 4, further comprising steps of: assigning a weight to each license plate identification result according to a time sequence of all the license plate identification results; and when, for each group, a weighted sum of one sub-identification result is greater than the threshold value, generating the final license plate identification result according to the sub-identification results. ### Claim 6 A license plate identification system, comprising: an image capturing apparatus configured to capture at least one raw image; and a processor configured to: receive the raw image from the image capturing apparatus; extract a historical background image through a foreground and background subtraction module; compare the raw image with the historical background image to determine the amount of image change; determine whether the amount of image change is greater than a predetermined value; when the amount of image change is greater than the predetermined value, generate a to-be-processed image comprising all of characters on a license plate according to the raw image; obtain a plurality of feature maps comprising character features of the to-be-processed image through a feature map extraction module; for each of the characters, extract a block and a coordinate according to the feature maps through a character identification model based on a neural network; and obtain a license plate identification result according to the respective blocks and the respective coordinates of the characters. ### Claim 7 (depends on 6) The license plate identification system as claimed in claim 6, wherein the processor is further configured to: obtain a vehicle front image or a vehicle rear image from the raw image through a vehicle front image capturing module or a vehicle rear image capturing module by using a first image feature and a first classifier; and obtain the to-be-processed image including all of the characters according to the vehicle front image or the vehicle rear image through a license plate character region detection model. ### Claim 8 (depends on 7) The license plate identification system as claimed in claim 7, wherein the processor is further configured to: obtain at least one character block from the vehicle front image or the vehicle rear image through the license plate character region detection model by using a second image feature and a second classifier; and determine a magnification according to the number of character blocks, wherein the to-be-processed image is obtained based on the character block according to the magnification. ### Claim 9 (depends on 6) The license plate identification system as claimed in claim 6, wherein the processor is further configured to: receive a plurality of license plate identification results; divide the plurality of license plate identification results into at least two groups according to a license plate grouping rule; vote for each sub-identification result in each of the groups; when, for each group, there is one sub-identification result having a voting score that is higher than a threshold value, generate a final license plate identification result according the sub-identification results; and update the character identification model according to the license plate identification result or the final license plate identification result, wherein the license plate grouping rule comprises a license plate naming grouping rule, an English character region and number character region grouping rule, a dash grouping rule, and a character relative position grouping rule. ### Claim 10 (depends on 9) The license plate identification system as claimed in claim 9, wherein the processor is further configured to: assign a weight to each license plate identification result according to a time sequence of all the license plate identification results; and when, for each group, a weighted sum of one sub-identification result is greater than the threshold value, generate the final license plate identification result according to the sub-identification results.