introduction
The development of driver's license recognition OCR technology makes the automatic processing of driver's license information possible. By using the OCR algorithm and API access, we can easily identify various fields on the driver's license, such as license number, name, gender, nationality, address, date of birth, date of initial issuance of the license, permitted driving type, expiration date, and license issuing agency wait.
This article will introduce the algorithm principle of OCR for driver's license recognition, and provide some sample codes to access the OCR API. By learning this knowledge, you will be able to understand how OCR for driver's license recognition works and apply this technology in your own applications.
Technical principle
Driver's license recognition OCR (Optical Character Recognition) is a technology that uses computer vision and pattern recognition technology to convert text information on a driver's license into editable or searchable text. The following is the general technical principle of driver's license recognition OCR:
Related algorithm introduction
In driver's license recognition OCR, algorithms that may be used include text detection algorithms and text recognition algorithms. Here are some commonly used algorithms and their brief introductions:
1. Edge detection algorithm
Algorithm introduction: The edge detection algorithm is used to identify the boundaries and contours in the image. Commonly used edge detection algorithms include Canny algorithm, Sobel algorithm and Laplacian algorithm. These algorithms determine edge locations by calculating the rate of change of pixel values in an image.
Application: In driver's license recognition OCR, the edge detection algorithm can be used to locate the boundary of the text area and help in text detection.
2. Convolutional Neural Network (CNN):
Algorithm introduction: CNN is a deep learning algorithm specially used for image processing and pattern recognition. It uses multiple convolutional and pooling layers to extract features from images for classification or recognition tasks.
Application: In the OCR of driver's license recognition, CNN can be used in the text recognition stage, by learning the characteristics of characters, and identifying the logo of each character from the text area.
3. Recurrent neural network (RNN):
Algorithm introduction: RNN is a recurrent neural network with memory function, which is suitable for processing sequence data. It is able to capture contextual information and sequence relationships, and is particularly useful for character recognition tasks.
Application: In driver's license recognition OCR, RNN can be used to process text sequences, recognize and connect each character to generate the final text result.
4. Support Vector Machine (SVM):
Algorithm introduction: SVM is a supervised learning algorithm commonly used in classification and recognition tasks. It divides data points into different categories by constructing an optimal hyperplane.
Application: In driver's license recognition OCR, SVM can be used to classify characters and recognize characters as corresponding signs.
These algorithms are only a part of OCR for driver's license recognition. In practical applications, multiple algorithms and technologies may be combined to improve accuracy and robustness. In addition, there are many other algorithms and techniques, such as template matching, feature extraction algorithms, etc., which can also be used in different aspects of OCR processing. The specific selection and application of the algorithm will be determined according to the actual situation and needs.
Application Scenario
Access the driver's license recognition OCR API in the program
In the Java program, we can directly copy the following code to access the driver's license recognition OCR API, and the API key can be obtained by registering and logging in on the APISpace website.
OkHttpClient client = new OkHttpClient().newBuilder().build();
MediaType mediaType = MediaType.parse("application/json");
RequestBody body = RequestBody.create(mediaType, "{"image":"","url":"","side":""}");
Request request = new Request.Builder()
.url("https://eolink.o.apispace.com/ocr-driving/driving-license")
.method("POST",body)
.addHeader("X-APISpace-Token","")
.addHeader("Authorization-Type","apikey")
.addHeader("Content-Type","application/json")
.build();
Response response = client.newCall(request).execute();
System.out.println(response.body().string());
return example
{
“words_result”: {
“lisenceNumber”: “2182821XXXXXXXXX4228”,
“name”: “王桃桃”,
“gender”: “女”,
“nationality”: “中国”,
“address”: “辽宁省大连市甘井子区”,
“birthday”: “1988-09-29”,
“firstIssueDate”: “2XXX-05-18”,
“class”: “C1”,
“validPeriod”: “2015-05-18至2021-XX-18”,
“issueOrganization”: “北京市公安局公安交通管理局”
},
“log_id”: “1664331400329230375895”
}
epilogue
With the further development of technology, driver's license recognition OCR will continue to be optimized and improved to improve accuracy, speed and adaptability. It will play a more important role in areas such as intelligent transportation systems, digital government services, and commercial applications. Friends in need, hurry up and use it~