Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 09/29/2024, 05/27/2025, and 08/06/2025 are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: METHOD OF ACQUIRING HIGH-DEFINITION LICENSE PLATE IMAGE AND ELECTRONIC DEVICE FOR PERFORMING THE SAME
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation "the corrected first alignment image" in line 5. There is insufficient antecedent basis for this limitation in the claim. Claims 8 and 9 depend from Claim 7 and thus are also rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 5, 7, 11, 13-15, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (AWFA-LPD: Adaptive Weight Feature Aggregation for Multi-frame License Plate Detection, published 2021) in view of Li et al. (Reading car license plates using deep neural networks, published 2018).
Regarding Claim 1, Lu teaches “A method of generating a license plate image of a vehicle performed by an electronic device, the method comprising:
generating a plurality of (Lu, Section 2.1, paragraph 2 discloses “The inputs of the entire network are video frames {𝐼𝑖 } ,𝑖 = 1,. . .,∞, and each frame will pass through the shared convolutional neural networks (CNNs) to get the feature maps {𝑓𝑖}”; see Fig. 2 of Lu, where images contain a license plate area and a target vehicle);
“determining a reference (see Fig. 2 of Lu and Fig. 2 caption: “obtain optical flow feature maps between surrounding frames and reference frame Ii”; where reference frame Ii is a reference image);
“calculating a plurality of optical flow values between the reference (Lu, Section 2.2 discloses
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; where optical flow features Nflow(Ii, Ij) are optical flow values between reference image Ii and each of plurality of images Ij);
“determining a plurality of alignment images among the plurality of (Lu, Section 2.3 discloses “In this part, we design a feature aggregation module with adaptive weight, which can assign different weight to the feature maps of adjacent frames and aggregate them with the feature maps 𝑓i of the reference frame 𝐼i into a new feature map 𝐹i”; where a new feature map Fi is a plurality of alignment images (Fi is one new feature map, with a plurality of feature maps resulting from all i) ); “and
generating the license plate image based on the plurality of alignment images” (Lu, Section 2.3 discloses
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; Lu, Section 2.4 also discloses “𝐹𝑖 input to the detection network will output the bounding boxes(bboxes) and scores of LPs in the reference frame”; where LPs are license plates (Lu, Introduction line 1); where locating LPs in the reference frame and outputting the bboxes of LPs is generating a license plate image based on the plurality of alignment images; see Fig. 2, where outputs are license plate cropped images).
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Figure 2 of Lu
Although Lu is directed to cropping license plate images, Lu does not explicitly teach “generating a plurality of crop images comprising a license plate area of a target vehicle based on a plurality of basic images of a video capturing the target vehicle” and “determining a reference crop image among the plurality of crop images.” That is, Lu does not explicitly teach cropping license plate images before applying the AWFA-LPD network above (see Figure 2).
However, in an analogous field of endeavor, Li discloses “generating a plurality of crop images comprising a license plate area of a target vehicle based on a plurality of basic images of a video capturing the target vehicle” and “determining a reference crop image among the plurality of crop images” (Li, section 3.2, paragraph 3 discloses “For license plate detection, the first phase is to generate candidate license plate bounding boxes with a high recall”; where first generating candidate license plate bounding boxes is generating a plurality of crop images. Li, section 3.2, paragraph 2 discloses “Considering the strong connectivity of characters in vertical direction than in horizontal direction, we perform vertical edge detection on the cropped license plate images using Sobel operator”; thus, resulting bounding boxes are “cropped license plate images.” Finally, see Fig. 4, where an input image is a cropped image, before further processing).
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Fig. 4 of Li
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified Lu to incorporate the teachings of Li by performing an initial cropping before further processing license plate images. The prior art Lu contained a ‘base’ method upon which the claimed invention can be seen as an ‘improvement’; Lu contained a license plate detection method using videos or multi-frame images as input to an optical flow method. The claimed invention recites using crop images before applying optical flow methods. The prior art Li contained a ‘comparable’ method that has been improved in the same way as the claimed invention; Li teaches first cropping input images before adjusting crops and recognizing license plate number. One of ordinary skill in the art could have applied the known ‘improvement’ technique of cropping images before further processing in the same way to the ‘base’ method of Li and the results would have been predictable. That is, one of ordinary skill in the art could have applied the optical flow method of Li on cropped images as taught by Lu with predictable results of license plate bounding boxes obtained from cropped input images. Additionally, a reference crop image obtained from among a plurality of crop images is taught by the combination of Lu and Li because the use of the technique of first cropping input images, applied to the method of Lu, would yield a reference crop image because the plurality of images from which the reference image is chosen are cropped, per the technique of Li; the result would have been obvious to one of ordinary skill in the art. Accordingly, the combination of Lu and Li discloses the invention of Claim 1.
Regarding Claim 3, the combination of Lu and Li discloses “The method of claim 1, wherein any one of the plurality of crop images is determined as the reference crop image based on an image generation time related to each of the plurality of crop images” (Lu, Section 2.2 discloses “In our proposed AWFA-LPD, we utilize ResNet-50 [4] as our back-bone network to extract features from video frames” and
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; where i and j are sequences of video frames, and thus a reference frame Ii is a reference crop image based on an image generation time i).
Regarding Claim 5, the combination of Lu and Li teaches “The method of claim 1, wherein the determining the plurality of alignment images comprises:
determining an error image among the plurality of crop images based on the plurality of optical flow values” (Lu, Section 2.2 discloses
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; where fj[Wingdings font/0xE0]i is an error image based on the plurality of optical flow values among the plurality of crop images based on the optical flow values Nflow(Ii, Ij); where, under the broadest reasonable interpretation, an error image may be a feature map warped from a neighbor frame to a reference frame. Lu, Section 2.2 also discloses “It is worth mentioning that the extracted feature maps {𝑓𝑖 } are not spatially aligned due to the motion of the objects in videos” and “we utilize an optical flow alignment module to correct the feature maps {𝑓𝑖 } before aggregating them”) ); “and
determining the plurality of alignment images based on the error image” (Lu, Section 2.3 discloses “In this part, we design a feature aggregation module with adaptive weight, which can assign different weight to the feature maps of adjacent frames and aggregate them with the feature maps 𝑓i of the reference frame 𝐼i into a new feature map 𝐹i”; where a new feature map Fi is a plurality of alignment images. See also equation (4), where Fi is obtained from fj[Wingdings font/0xE0]i
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).
Regarding Claim 7, the combination of Lu and Li discloses “The method of claim 1, wherein the generating the license plate image comprises:
determining a first alignment image requiring correction among the plurality of alignment images” (Lu, Section 2.3 discloses “In this part, we design a feature aggregation module with adaptive weight, which can assign different weight to the feature maps of adjacent frames and aggregate them with the feature maps 𝑓i of the reference frame 𝐼i into a new feature map 𝐹i”; where a feature map fj is a first alignment image);
“correcting the first alignment image and generating the corrected first alignment image” (Lu, Section 2.3 discloses “In this part, we design a feature aggregation module with adaptive weight, which can assign different weight to the feature maps of adjacent frames and aggregate them with the feature maps 𝑓i of the reference frame 𝐼i into a new feature map 𝐹i”; where new feature map Fi is a corrected first alignment image); “and
generating the license plate image based on the plurality of alignment images comprising the corrected first alignment image” (Lu, Section 2.3 discloses
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; Lu, Section 2.4 also discloses “𝐹𝑖 input to the detection network will output the bounding boxes(bboxes) and scores of LPs in the reference frame”; where LPs are license plates (Lu, Introduction line 1); where locating LPs in the reference frame and outputting the bboxes of LPs is generating a license plate image based on the plurality of alignment images; see Fig. 2, where outputs are license plate cropped images).
Regarding Claim 11, the combination of Lu and Li teaches “The method of claim 1, wherein the generating the license plate image comprises post-processing the license plate image based on an image enhancement algorithm” (Lu, Figure 2 caption recites “Finally, NLS is used to post-process the detection results.” Lu, Section 2.4, paragraph 3 discloses “If there is a bbox that cannot connect to any bboxes of adjacent frames, remove it. The function of NLS is to suppress the number of isolated FP samples, effectively increasing precision”; where NLS is a Non-link Suppression Mechanism).
Regarding Claim 13, the combination of Lu and Li teaches “The method of claim 1, further comprising determining a license plate number of the target vehicle based on the generated license plate image” (Li, Section 4, paragraph 1 discloses “The second stage of LPDR system is to recognize the characters in the license plate.”) The proposed combination as well as the motivation for combining the Lu and Li references presented in the rejection of Claim 1, apply to Claim 13 and are incorporated herein by reference. Thus, the apparatus recited in Claim 13 is met by Lu and Li.
Regarding Claim 14, the combination of Lu and Li teaches “A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1” (Lu, Section 3.1, paragraph 2 discloses “We make all experiments on a computer with an Intel Core 3.4GHz CPU, 12GB of RAM and four NVIDIA 1080TiGPU.”)
Regarding Claims 15, 17, and 19, Claims 15, 17, and 19 recite a system with elements corresponding to the steps recited in Claims 1, 3, and 5, respectively. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine Lu and Li references, presented in rejection of Claim 1, apply to this claim. Finally, the combination Lu and Li references discloses “An electronic device comprising: at least one processor; and a memory configured to store instructions” (Lu, Section 3.1, paragraph 2 discloses “We make all experiments on a computer with an Intel Core 3.4GHz CPU, 12GB of RAM and four NVIDIA 1080TiGPU.”)
Claims 2 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (AWFA-LPD: Adaptive Weight Feature Aggregation for Multi-frame License Plate Detection, published 2021) in view of Li et al. (Reading car license plates using deep neural networks, published 2018), further in view of Cohen (US 8369653 B1).
Regarding Claim 2, the combination of Lu and Li discloses “The method of claim 1, wherein the generating the plurality of crop images comprises:
determining the license plate area of the target vehicle in a first basic image” (Li, Section 3.1 discloses “After getting these saliency maps, the candidate bounding boxes are generated independently at each scale by using the run length smoothing algorithm (RLSA) [24] and connected component analysis (CCA)”);
“generating a first basic crop image comprising the determined license plate area” (Li, Section 3.1 discloses “For license plate detection, the first phase is to generate candidate license plate bounding boxes with a high recall.”; where a candidate license plate bounding box is a first basic crop image); “and
The combination of Lu and Li does not explicitly teach “generating a first crop image by up-sampling the first basic crop image.”
However, in an analogous field of endeavor, Cohen teaches “generating a first crop image by up-sampling the first basic crop image” (Cohen, Column 15, line 64- column 16, line 2 discloses “FIG. 6A illustrates a low-resolution input image, license plate. FIG. 6B illustrates the results of upsampling license plate using bicubic interpolation, while FIG. 6C illustrates the result of upsampling license plate using the methods described above that are dependent on sparse derivative priors on first and second derivatives”).
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified the combination of Lu and Li to incorporate the teachings of Cohen by performing upsampling on license plate images. One of ordinary skill in the art would be motivated to combine the Lu, Li, and Cohen references in order to increase resolution: Cohen, column 1, lines 13-18 discloses "Some applications provide an upsampling operation, sometimes referred to as a "super-resolution" operation, by which the resolution of images can be increased. Increasing the resolution of a digital image is a fundamental operation in image processing.” Accordingly, the combination of Lu, Li, and Cohen discloses the invention of Claim 2.
Regarding Claim 16, Claim 16 recites a system with elements corresponding to the steps recited in Claim 2. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine Lu, Li, and Cohen references, presented in rejection of Claim 2, apply to this claim. Finally, the combination Lu, Li, and Cohen references discloses “An electronic device comprising: at least one processor; and a memory configured to store instructions” (Lu, Section 3.1, paragraph 2 discloses “We make all experiments on a computer with an Intel Core 3.4GHz CPU, 12GB of RAM and four NVIDIA 1080TiGPU.”)
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (AWFA-LPD: Adaptive Weight Feature Aggregation for Multi-frame License Plate Detection, published 2021) in view of Li et al. (Reading car license plates using deep neural networks, published 2018), further in view of Abhiram et al. (US 2019/0287297 A1).
Regarding Claim 4, the combination of Lu and Li does not explicitly teach the method of Claim 4.
However, in an analogous field of endeavor, Abhiram discloses “The method of claim 1, wherein the calculating the plurality of optical flow values comprises:
calculating a first position change between a first pixel of the reference crop image and a second pixel of a first crop image corresponding to the first pixel” (Abhiram, [0018] discloses “The optical flow between the current scene frame 204 and a matching key frame 212 is the shifting of corresponding pixels between the key frame and the current scene frame”); and
calculating a first optical flow value of the first crop image based on the first position change” (Abhiram, [0018] discloses “Optical flow representation 210 may be, for example, an array of vectors corresponding to the change in position for each pixel in a scene frame.”)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified the combination of Lu and Li to incorporate the teachings of Abhiram by determining optical flow representation comprising vectors by determining pixel to pixel shifting. One of ordinary skill in the art would be motivated to combine the Lu, Li, and Abhiram references in order to identify and track objects: Abhiram, [0018] discloses “Computing optical flow enables tracking and identifying moving objects in the environment.” Accordingly, the combination of Lu, Li, and Abhiram discloses the invention of Claim 4.
Regarding Claim 18, Claim 18 recites a system with elements corresponding to the steps recited in Claim 2. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine Lu, Li, and Abhiram references, presented in rejection of Claim 4, apply to this claim. Finally, the combination Lu, Li, and Abhiram references discloses “An electronic device comprising: at least one processor; and a memory configured to store instructions” (Lu, Section 3.1, paragraph 2 discloses “We make all experiments on a computer with an Intel Core 3.4GHz CPU, 12GB of RAM and four NVIDIA 1080TiGPU.”)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (AWFA-LPD: Adaptive Weight Feature Aggregation for Multi-frame License Plate Detection, published 2021) in view of Li et al. (Reading car license plates using deep neural networks, published 2018), further in view of Zhu et al. (US 2023/0004747 A1).
Regarding Claim 10, the combination of Lu and Li does not explicitly teach the method of Claim 10.
However, in an analogous field of endeavor, Zhu teaches “The method of claim 1, wherein the generating the license plate image comprises:
obtaining intensity values of pixels comprising the license plate area of each of the plurality of alignment images” (Zhu, [0045] discloses “In an embodiment, assuming the image is an RGB license plate image, this image can be converted into a grayscale image first. Then, statistical data of an image is used, e.g., using a histogram representation of the image.”; where grayscale image values are intensity values of pixels);
“determining pixels corresponding to a preset intensity value among the obtained intensity values” (Zhu, [0045] discloses “Then a threshold value is used to separate the image into two classes, e.g., a first class representing image pixel intensity values less than a threshold (e.g., image background or foreground), and a second class representing image pixel intensity values greater than a threshold (e.g., image foreground or background)”; where a threshold value is a preset intensity value); “and
generating the license plate image based on the determined pixels” (Zhu, [0046] discloses “An initial or first threshold “Thresh 1” is applied in the Otsu method resulting in a low resolution image 530 which characters are still adhered and subject to further discrimination”; see Fig. 7).
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It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified the combination of Lu and Li to incorporate the teachings of Zhu by performing intensity thresholding on license plate images. One of ordinary skill in the art would be motivated to combine the Lu, Li, and Zhu references in order to separate background pixels from character (license plate number) pixels: Zhu, [0045] discloses “In particular, at 120, FIG. 3, Otsu methods are run to iterate through possible threshold values and calculate a measure of spread for the pixel levels each side of the threshold, i.e., the pixels that either fall in foreground or background.” Accordingly, the combination of Lu, Li, and Cohen discloses the invention of Claim 10.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (AWFA-LPD: Adaptive Weight Feature Aggregation for Multi-frame License Plate Detection, published 2021) in view of Li et al. (Reading car license plates using deep neural networks, published 2018), further in view of Seungho et al. (KR 102556036 B1).
Regarding Claim 12, the combination of Lu and Li does not explicitly teach the method of Claim 11.
However, in an analogous field of endeavor, Seungho teaches “The method of claim 11, wherein the image enhancement algorithm comprises a contrast-limited adaptive histogram equalization (CLAHE) algorithm” (Seungho, page 5, paragraph 2 discloses “In addition, the image correction unit 28 corrects the actual license plate image generated by the license plate extraction unit 27 to easily recognize characters belonging to the actual license plate image, and generates a standard license plate image.” Seungho, page 5, paragraph 13 discloses “Further, the contrast enhancement unit 28d applies a Contrast-Limited Adaptive Histogram Equalization (CLAHE) technique to the brightness signal processed by the sharpening filter unit 28c to increase the contrast of the brightness signal.”)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified the combination of Lu and Li to incorporate the teachings of Seungho by applying a CLAHE technique to increase contrast. One of ordinary skill in the art would be motivated to combine the Lu, Li, and Zhu references in order to allow characters to be recognized more easily: Seungho, page 5, paragraph 2 discloses “In addition, the image correction unit 28 corrects the actual license plate image generated by the license plate extraction unit 27 to easily recognize characters belonging to the actual license plate image, and generates a standard license plate image.” Accordingly, the combination of Lu, Li, and Seungho discloses the invention of Claim 12.
Allowable Subject Matter
Claim 6 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 8 and 9 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 6, the combination of Lu and Li teaches “The method of claim 5, wherein the determining the error image comprises:
determining a first crop image and a second crop image adjacent to the first crop image among the plurality of crop images” (Lu, Section 2.2 discloses
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; where feature maps j are from video frames, particularly a sequence from j[Wingdings font/0xE0]i, and thus Lu teaches a first crop image and a second crop image adjacent to the first crop image);
calculating a difference between a first optical flow value of the first crop image and a second optical flow value of the second crop image” (Wu (CN 117676238 A) page 10, paragraph 2 discloses “if the difference value between the first optical flow value and the second optical flow value is greater than or equal to the third threshold value, the ith frame image frame is determined as the key frame image”); “and
Wu discloses identifying a key frame by subtracting optical flow values (Wu, page 10, paragraph 2 discloses “if the difference value between the first optical flow value and the second optical flow value is greater than or equal to the third threshold value, the ith frame image frame is determined as the key frame image.”) However, even under the broadest reasonable interpretation, a key frame cannot be construed to be an “error image,” and thus Wu does not explicitly teach “determining whether the first crop image is the error image based on the difference.”
Thus, none of the cited prior art, alone or in combination, provides a motivation to teach the ordered combination of “The method of claim 5, wherein the determining the error image comprises:
determining a first crop image and a second crop image adjacent to the first crop image among the plurality of crop images;
calculating a difference between a first optical flow value of the first crop image and a second optical flow value of the second crop image; and
determining whether the first crop image is the error image based on the difference.”
Regarding Claim 20, Claim 20 recites a system with elements corresponding to the steps recited in Claim 6, and thus contains all allowable subject matter of Claim 6.
Regarding Claim 8, none of the previously cited prior art explicitly teaches the method of Claim 8. Although bilinear approximation is known in the art of image processing (Hereld et al. (US 2004/0085256 A1), [0053] discloses “As indicated in a block 1100, positions of dots in a tile are predicted from the test image description and measurement of the four registration marks, for example using bilinear approximation”), none of the previously cited prior art explicitly teaches using bilinear approximation to determine if an image requires correction.
Thus, none of the previously cited prior art references provides a motivation to teach the ordered combination of “The method of claim 7, wherein the determining the first alignment image requiring the correction comprises: determining whether the license plate area of each of the plurality of alignment images satisfies bilinear approximation; and determining that the first alignment image that does not satisfy the bilinear approximation requires correction.”
Regarding Claim 9, none of the previously cited prior art explicitly teaches the method of Claim 9. Although bilinear approximation is known in the art of image processing (Hereld et al. (US 2004/00825256 A1) discloses “As indicated in a block 1100, positions of dots in a tile are predicted from the test image description and measurement of the four registration marks, for example using bilinear approximation”), none of the previously cited prior art explicitly teaches using bilinear interpolation to correct coordinate values of corners of the license plate area, and thus generate an alignment image based on the coordinate values.
Thus, none of the previously cited prior art references provides a motivation to teach the ordered combination of “The method of claim 7, wherein the generating the corrected first alignment image comprises:
calculating coordinate values in the first alignment image of corners of the license plate area of the first alignment image;
calculating corrected coordinate values for each of the coordinate values of the corners based on bilinear approximation; and
generating the corrected first alignment image by correcting the first alignment image based on the corrected coordinate values.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Ma et al. (CN 101154270 A) discloses a binarization method for automobile license images.
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/CAROLINE TABANCAY DUFFY/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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