Mobile type of automatic identification system for a car plate

• US 5,425,108 A
• Filed: 09/22/1993
• Issued: 06/13/1995
• Est. Priority Date: 09/04/1992
• Status: Expired due to Term

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Chen, Wen-Yu, Hwang, Chung-Mu, Shu, Shyh-Yeong
• Primary Examiner(s): NOT, DEFINED
• Assistant Examiner(s): NOT, DEFINED
• Application Number:
  US08/124,445
• Time in Patent Office: 629 Days
• Field of Search: 382/1, 382/14, 382/15, 382/9, 382/28, 382/48, 382/39, 340/932.2, 340/933, 340/937, 395/21, 395/61
• US Class Current: 382/105
• CPC Class Codes: G06K 7/14 using light without selection of wavelength, e.g. sensing reflected white light G06K7/10831 - G06K7/1097 take precedence
• CPC Class Codes: G06K 7/1482 using fuzzy logic or natural solvers, such as neural networks, genetic algorithms and simulated annealing
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/148 Segmentation of character regions

Abstract

An automatic identification system for a car-plate which comprises a photographing apparatus and an image-processing CPU. The system disclosed in the present invention can be mounted in a car for automatically identifying a car-plate that is under a still or moving condition. The photographing apparatus is used for taking the image of a car-plate, and the image is then transmitted into the image-processing CPU, which accurately extracts the characters of a car-plate using a "Fuzzy inference" technique, and identifies the characters through a character structure analysis neural network. With the automatic identification system of the present invention, an incorrect identification can be avoided in the event the image of a car-plate is blurred, and the characters is deformed or smeared.

Claims

1. An automatic identification method for a cap-plate comprising a photographing apparatus adapted to take a picture of the car-plate while under still or moving condition, an image-processing CPU, and means for transmitting said picture into said image-processing CPU for converting said picture into a digital image comprising a plurality of pixels arranged in a plurality of columns by a plurality of rows, each pixel being represented by a gray level, and for automatic identification of characters contained in said car-plate;

wherein said automatic identification of characters performed by said image-processing CPU comprises the steps of;

(a) finding the position of the car-plate in said image and extracting a partial image corresponding to said car-plate using an appropriate methodology;

(b) calculating a vertically projected array of said partial image containing said car-plate by summing the gray levels of all the pixels in a same column, and storing the result so calculated into a one-dimensional array X;

(c) calculating a first differential vertically projected array dx by performing a first difference process of said partial image containing said car plate as obtained in step (a) and summing values of calculated differences in each column to form said array dx, said first difference process is formed by subtracting from the gray level of each pixel thereof by the gray level of a neighboring pixel in the next column but on the same row and taking the absolute value of the difference;

(d) establishing a fuzzy rule base by forming a membership function which is a function of X and dx obtained in steps (b) and (c) above;

(e) using a max-min inference procedure in accordance with said fuzzy rule base to select a plurality of best dividing points to thereby frame and extract every individual character contained in said car-plate;

(f) converting each extracted character into a plurality of pixels, which are then grouped into three pixel groups;

a full shape group, a top shape group and a bottom shape group, wherein said full shape group contains all the pixels corresponding to said extracted character, and said top shape and bottom shape groups each containing only a portion of said pixels;

(g) forming a four-layer neural network containing four layers;

an input layer, a character structure layer, an implicit layer, and an output layer, said input layer containing portions corresponding to said full shape group, said top shape group and said bottom shape group obtained in step (f); and

(h) using said neural network from step (g) to identify all said characters extracted in step (e).

2. The automatic identification method for a car-plate of claim 1 wherein said fuzzy rule base consists of the following rules:

(a) for a point P_p when X is j is large and dx_j is small, then said membership function, which corresponds to the probability of said point P_j being a boundary, is large;

(b) when X_j is small and dx_j is large, then said membership function is small;

(c) when X_j is large and dx_j is large, then said membership function is middle; and

(d) when X_j is small and dx_j is small, then said membership function is middle.

3. The automatic identification method for a car-plate of claim 2 wherein:

(a) said expression "X_j is large" is represented by a monotonically increasing linear function, and said expression "X_j is small" is represented by a monotonically decreasing linear function;

(b) said expression "dx_j is large" is represented by a monotonically increasing linear function, and said expression "dx_j is small" is represented by a monotonically decreasing linear function;

(c) said membership function is represented by a triangular function.

4. The automatic identification method for a car-plate as claimed in claim 1;

wherein;

said character structure layer of said neural network includes three portions;

a full shape portion, a top shape portion and a bottom shape portion;

said input layer and said character structure layer being partially connected in such a manner that every neural element in said full shape portion of said character structure layer is connected with all neural elements in said full shape portion of said input layer, every neural element in said top shape portion of said character structure layer is connected with all neural elements in said top shape portion of said input layer, and every neural element in said bottom shape portion of said character structure layer is connected with all neural elements in said bottom shape portion of said input layer;

said character structure layer and said implicit layer fully connected with neural chains; and

said implicit layer and said output layer are fully connected with neural chains.

5. The automatic identification method for a car-plate as claimed in claim 1 which is adapted to be installed and operable in a mobile car.

6. The automatic identification method for a car-plate as claimed in claim 5 wherein said image-processing CPU and said photographing apparatus are powered by a power supply provided in said mobile car.