# US 7,613,328 B2 · Label detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,613,328 B2 - Document ID: US7613328B2 - Status: Granted - Kind: B2 - Application number: 11/223,885 - Filed: 2005-09-09 - Published: 2007-03-15 - Granted: 2009-11-03 - Earliest claimed: 2005-09-09 - Assignees: Honeywell International Inc. - Inventors: Lokesh R. Boregowda; Anupama Rajagopal; Krishnamoorthy Dornadula; Lalitha M. Eswara - Web page: https://patentgrep.com/patent/US7613328B2 ## CPC - G06V 20/63: {Scene text, e.g. street names} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A system and a method to detect a label are described herein. The system and method are to extract a horizontal region of interest based on the image; to extract a vertical region of interest based on the image; and to identify presence of the primary label in the image from the horizontal region of interest and from the vertical region of interest. ## Claims ### Claim 1 A method to detect an identification label comprising: using a processor for determining an input image having an identification label; processing the input image; determining a column difference image; determining a row projection histogram of the column difference image associated with the identification label; processing a row projected image to extract a horizontal region associated with the identification label; extracting a vertical region of interest associated with the identification label, wherein an index of a largest cluster present is used to extract the vertical region; determining vertical wavelet coefficients using a wavelet transform and clustering the vertical wavelet coefficients; wherein the extracting the horizontal region of interest is selected from a group including: determining a column projection histogram of a row difference image associated with the identification label and processing a column projected image; and using wavelet coefficient clustering of the row difference image. ### Claim 2 (depends on 1) The method of claim 1 further comprising: identifying the identification label from the vertical region and from the horizontal region; and performing character recognition on the identification label. ### Claim 3 (depends on 1) The method of claim 1 wherein using the wavelet coefficient clustering includes preprocessing vertical detailed coefficients and clustering the vertical detailed coefficients. ### Claim 4 (depends on 1) The method of claim 1 wherein processing the input image is selected from a group including image enhancement, brightness correction, and contrast correction. ### Claim 5 A method to identify presence of a primary label in an image comprising: using a processor for extracting a vertical region of interest based on the image using projection histogram analysis and an index of a largest cluster present; extracting a horizontal region of interest based on the image using projection histogram analysis; determining vertical wavelet coefficients using a wavelet transform and clustering the vertical wavelet coefficients; and identifying presence of the primary label in the image from the horizontal region of interest and from the vertical region of interest. ### Claim 6 (depends on 5) The method of claim 5 wherein extracting the horizontal region of interest based on the image includes column difference image analysis and row projection histogram analysis. ### Claim 7 (depends on 5) The method of claim 5 wherein extracting the vertical region of interest based on the image includes row difference image analysis and column projection histogram analysis. ### Claim 8 (depends on 5) The method of claim 5 wherein extracting the vertical region of interest based on the image includes wavelet coefficient clustering. ### Claim 9 (depends on 5) The method of claim 5 further comprising pre-processing the image to enhance the primary label before extracting the vertical region of interest. ### Claim 10 (depends on 9) The method of claim 9 wherein the pre-processing is selected from a group including brightness correction and contrast correction. ### Claim 11 (depends on 9) The method of claim 9 wherein the pre-processing includes: subjecting the image to zero meaning; and stretching the histogram. ### Claim 12 (depends on 5) The method of claim 5 wherein the primary label includes an orientation of up to about 20 degrees from a horizontal. ### Claim 13 A tangible machine-readable storage medium storing a sequence of instructions that, when executed by a computer, cause the computer to perform a method of identifying presence of a primary label in an image, the method comprising: extracting a vertical region of interest based on an image using projection histogram analysis and an index of a largest cluster present; extracting a horizontal region of interest based on the image using projection histogram analysis; determining vertical wavelet coefficients using a wavelet transform and clustering the vertical wavelet coefficients; and identifying presence of a primary label in the image from the horizontal region of interest and from the vertical region of interest. ### Claim 14 A method comprising: using a processor for detecting a vehicle in an image; extracting a vertical region of interest based on the image using projection histogram analysis and an index of a largest cluster present; determining vertical wavelet coefficients using a wavelet transform and clustering the vertical wavelet coefficients; detecting a license plate of the vehicle using the projection histogram analysis on a gradient image; segmenting characters of the plate; and recognizing the characters. ### Claim 15 (depends on 14) The method of claim 14 further comprising locating presence of characters in the primary label. ### Claim 16 (depends on 15) The method of claim 15 wherein the image is selected from a group including a gray scale image version and a Y channel image version. ### Claim 17 A system to identify a label in an image comprising: means for extracting a vertical region of interest based on the image using projection histogram analysis and an index of a largest cluster present; means for extracting a horizontal region of interest based on the image using projection histogram analysis; means for determining vertical wavelet coefficients using a wavelet transform and clustering the vertical wavelet coefficients; and means for identifying presence of the label in the image from the horizontal region of interest and from the vertical region of interest. ### Claim 18 (depends on 17) The system of claim 17 wherein the image includes an object having the label, wherein the system requires the object to slow down and/or stop when the image is taken. ### Claim 19 (depends on 17) The system of claim 17 wherein means for extracting the vertical region includes removing one-fourth of a row in a top part of the image, removing one-eighth of a row at a bottom part of the image; and removing one-eighth of columns at both sides of the image. ### Claim 20 (depends on 17) The system of claim 17 wherein means for extracting the horizontal region includes an adaptive threshold to determine substantially exact row location where the label is present. ### Claim 21 (depends on 17) The system of claim 17 wherein means for extracting the vertical region uses row differencing and includes an adaptive threshold to prune the image to substantially exact columns.