How to accurately identify the real official seal on a contract using Java technology

How to use Java technology to accurately identify the real official seal on the contract

1. Introduction
   The official seal plays an extremely important role in the contract, it represents The legal exercise of public power and the formal recognition of the enterprise. However, with the development of technology, the problem of forging official seals has gradually become prominent. This article introduces an implementation method that uses Java technology to accurately identify the real official seal on a contract, and ensures the authenticity and legality of the official seal through digital image processing and machine learning algorithms.
2. Image preprocessing
   Before starting to recognize the official seal, we need to preprocess the contract image to improve the accuracy of the subsequent algorithm. Preprocessing mainly includes image binarization, noise removal and edge detection.

2.1. Image binarization
Contract images are generally in color, but official seals are usually in black and white. Therefore, we need to convert the color image into a binary image to better extract the features of the official seal. This can be achieved using the binarization function in the OpenCV library:

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.core.Size;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

public class ImageBinarization {
    public static void main(String[] args) {
        // 加载OpenCV库
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        
        // 读取合同图像
        Mat image = Imgcodecs.imread("contract.jpg");
        
        // 转换为灰度图像
        Mat grayImage = new Mat();
        Imgproc.cvtColor(image, grayImage, Imgproc.COLOR_BGR2GRAY);
        
        // 二值化
        Mat binaryImage = new Mat();
        Imgproc.threshold(grayImage, binaryImage, 0, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
        
        // 保存二值化图像
        Imgcodecs.imwrite("binary_image.jpg", binaryImage);
    }
}

2.2. Noise removal
Since the contract image may have some noise, such as grain and texture during scanning or shooting, we need to Perform some processing on the value image to remove these noises. You can use the open operation in the OpenCV library to achieve:

import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.core.Size;
import org.opencv.core.Scalar;
import org.opencv.imgproc.Imgproc;

public class NoiseRemoval {
    public static void main(String[] args) {
        // 读取二值化图像
        Mat binaryImage = Imgcodecs.imread("binary_image.jpg", Imgcodecs.IMREAD_GRAYSCALE);
        
        // 进行开操作
        Mat noiseRemovedImage = new Mat();
        Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3, 3));
        Imgproc.morphologyEx(binaryImage, noiseRemovedImage, Imgproc.MORPH_OPEN, kernel);
        
        // 保存去噪声图像
        Imgcodecs.imwrite("noise_removed_image.jpg", noiseRemovedImage);
    }
}

2.3. Edge detection
Edge detection is a key step in identifying official seals. It can be implemented using the Canny algorithm in the OpenCV library:

import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.core.Size;
import org.opencv.core.Scalar;
import org.opencv.imgproc.Imgproc;

public class EdgeDetection {
    public static void main(String[] args) {
        // 读取去噪声图像
        Mat noiseRemovedImage = Imgcodecs.imread("noise_removed_image.jpg", Imgcodecs.IMREAD_GRAYSCALE);
        
        // 进行边缘检测
        Mat edges = new Mat();
        Imgproc.Canny(noiseRemovedImage, edges, 100, 200);
        
        // 保存边缘图像
        Imgcodecs.imwrite("edges.jpg", edges);
    }
}

3. Official seal recognition
   After the image preprocessing is completed, we can start official seal recognition. Here we use machine learning algorithms to achieve accurate recognition of official seals through feature training and classifier construction. A commonly used machine learning algorithm is Support Vector Machine (SVM).

3.1. Feature extraction
First, we need to extract some features from the edge images for training and classification. Commonly used features include shape, texture, and color. Here we take shape features as an example, using contour detection in the OpenCV library to extract the shape features of the official seal:

import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.core.Size;
import org.opencv.core.Scalar;
import org.opencv.imgproc.Imgproc;

public class ShapeFeatureExtraction {
    public static void main(String[] args) {
        // 读取边缘图像
        Mat edges = Imgcodecs.imread("edges.jpg", Imgcodecs.IMREAD_GRAYSCALE);
        
        // 检测轮廓
        List<MatOfPoint> contours = new ArrayList<>();
        Mat hierarchy = new Mat();
        Imgproc.findContours(edges, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
        
        // 提取轮廓特征
        double[] features = new double[contours.size()];
        for (int i = 0; i < contours.size(); i++) {
            features[i] = Imgproc.contourArea(contours.get(i));
        }
        
        // 打印轮廓特征
        for (double feature : features) {
            System.out.println("Contour feature: " + feature);
        }
    }
}

3.2. Training and classification
Next, we use the extracted features for training and classification. First, we need to prepare some labeled official seal images as training samples. Then, the extracted features and corresponding tags are trained by the machine learning algorithm to build an official seal classifier. In the recognition stage, the features of the contract image to be recognized are extracted, and then the trained classifier is used to make classification judgments.

Due to the complexity of the complete code for training and classification, we cannot show them one by one here, but you can refer to the official OpenCV documentation and related tutorials to use machine learning algorithms such as support vector machines for training and classification.

4. Conclusion
   Through the method introduced in this article, we can use Java technology to accurately identify the real official seal on the contract. First, the contract image is preprocessed, including binarization, noise removal, and edge detection. Then, a machine learning algorithm is used to extract the characteristics of the official seal, and a classifier for the official seal is trained and constructed. Finally, through feature extraction and classification judgment, the official contract seal is accurately identified.

However, it should be noted that although this method can improve the accuracy of official seal identification, it cannot 100% guarantee the authenticity and legality of the official seal. In practical applications, it is also necessary to combine other security measures and means to ensure the safety and effectiveness of the official seal.

References:

1. OpenCV official documentation: https://docs.opencv.org/
2. Machine Learning Practice: Scikit-Learn and TensorFlow (Author: Aurélien Géron, Translator: Tang Xuetao, Bao Jianqiang)

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