TW226454B - Pattern recognition method for car plate - Google Patents
Pattern recognition method for car plateInfo
- Publication number
- TW226454B TW226454B TW82109111A TW82109111A TW226454B TW 226454 B TW226454 B TW 226454B TW 82109111 A TW82109111 A TW 82109111A TW 82109111 A TW82109111 A TW 82109111A TW 226454 B TW226454 B TW 226454B
- Authority
- TW
- Taiwan
- Prior art keywords
- plate
- recognition
- image
- character
- layer
- Prior art date
Links
Landscapes
- Image Analysis (AREA)
- Character Discrimination (AREA)
Abstract
One type of car plate automatic recognition method consists of thefollowing procedures: 1. finding car plate position After the first-order difference operation is executed on the horizontalsatial frequency of car plate image, the possible clustering segmentationof plate position are linked by the group logic relation. The correspondingarea image of each group is processed with binary threshold, then combiningthe width and continuity of character stroke as the decision making base tofind the plate position. 2. segmenting the edge of plate (a). dividing the plate image into left and right area, executing thefirst-order difference operation and profile on the horizontal direction.The results of vertical first-order difference operation are processed withtriple threshold into three conditions in order to find the up edge(Ul, Ur)and down edge(Dl, Dr) of plate strings. (b). storing the vertical profile of plate area image in X array, thenstoring the results of binary threshold after horizontal differenceoperation in Dx array. After finding the most left and right three peakpositions, the optimal left and right position of plate strings can be gotby fuzzy theory. 3. plate character segmentation The horizontal gray level wave form between the up and down edge of platestrings is processed with adaptive binary threshold, then calculating thecounts of pixels with 0 gray value in each vertical line. The vertical linewith zero count is the likely position of boundary between the characters.By referring the average distance between the neiboughering characters, theoptimal character boundary can be adjusted by splitting and merging properly. 4. plate character recognition Inputting the segmented image array of each character into one neuralnetwork of feature fusion medium value operation, operating by thefollowing processing layer: (a). input layer: normalizing each character image to 16 x 16 image arraywith pixel's gray value as input data (b). input hidden layer: dividing into feature mapping layer and smoothmapping layer with inputted 3 x 3 pixel array as unit. The former is gotby backward propagation operation of neural network and the latter is gotfrom the convolution operation with smooth mask. (c). medium value operation network: processing the medium value operationwith the neural cell corresponding to the two mapping layers (d). recognition input layer: the output of medium value operation networkas inputs (e). recognition hidden layer: fully connected with recognition input layerand the value of neural cell is got by backward propagation operation ofneural network (f). recognition output layer: fully connected with recognition hiddenlayer and outputting the results of character recognition by backwardpropagation operation of neural network
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW82109111A TW226454B (en) | 1993-11-02 | 1993-11-02 | Pattern recognition method for car plate |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW82109111A TW226454B (en) | 1993-11-02 | 1993-11-02 | Pattern recognition method for car plate |
Publications (1)
Publication Number | Publication Date |
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TW226454B true TW226454B (en) | 1994-07-11 |
Family
ID=51348376
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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TW82109111A TW226454B (en) | 1993-11-02 | 1993-11-02 | Pattern recognition method for car plate |
Country Status (1)
Country | Link |
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TW (1) | TW226454B (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7851735B2 (en) | 2006-12-12 | 2010-12-14 | Technology Research Center National Applied Research Laboratories | Microscope automatic focusing device and method thereof |
TWI408612B (en) * | 2009-09-25 | 2013-09-11 | Ind Tech Res Inst | Method and system for dynamically and simultaneously determining the relative relation between moving objects |
US9286533B2 (en) | 2009-04-30 | 2016-03-15 | Industrial Technology Research Institute | Method for image recombination of a plurality of images and image identification and system for image acquiring and identification |
US9811775B2 (en) | 2012-12-24 | 2017-11-07 | Google Inc. | Parallelizing neural networks during training |
CN108780519A (en) * | 2016-03-11 | 2018-11-09 | 奇跃公司 | Structure learning in convolutional neural networks |
CN116740688A (en) * | 2023-08-11 | 2023-09-12 | 武汉市中西医结合医院(武汉市第一医院) | Medicine identification method and system |
-
1993
- 1993-11-02 TW TW82109111A patent/TW226454B/en not_active IP Right Cessation
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7851735B2 (en) | 2006-12-12 | 2010-12-14 | Technology Research Center National Applied Research Laboratories | Microscope automatic focusing device and method thereof |
US9286533B2 (en) | 2009-04-30 | 2016-03-15 | Industrial Technology Research Institute | Method for image recombination of a plurality of images and image identification and system for image acquiring and identification |
TWI408612B (en) * | 2009-09-25 | 2013-09-11 | Ind Tech Res Inst | Method and system for dynamically and simultaneously determining the relative relation between moving objects |
US9811775B2 (en) | 2012-12-24 | 2017-11-07 | Google Inc. | Parallelizing neural networks during training |
US10635966B2 (en) | 2012-12-24 | 2020-04-28 | Google Llc | System and method for parallelizing convolutional neural networks |
US11928577B2 (en) | 2012-12-24 | 2024-03-12 | Google Llc | System and method for parallelizing convolutional neural networks |
CN108780519A (en) * | 2016-03-11 | 2018-11-09 | 奇跃公司 | Structure learning in convolutional neural networks |
CN116740688A (en) * | 2023-08-11 | 2023-09-12 | 武汉市中西医结合医院(武汉市第一医院) | Medicine identification method and system |
CN116740688B (en) * | 2023-08-11 | 2023-11-07 | 武汉市中西医结合医院(武汉市第一医院) | Medicine identification method and system |
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