YOLOv8 营业执照提取 统一社会信用代码、企业名称

企业名称"/>

YOLOv8 营业执照提取 统一社会信用代码、企业名称

目录

背景

尝试一：整图OCR识别，然后正则匹配

尝试二：利用显著特征，直接传统方法定位，切出来识别

尝试三：yolov8训练一个统一社会信用代码、企业名称位置检测

​编辑

效果

模型信息

项目

​编辑

代码

下载

其他

背景

        因项目需要，需要从营业执照中提取统一社会信用代码、企业名称。

尝试一：整图OCR识别，然后正则匹配

        统一社会信用代码大多情况是18位数字加英文的组合，比较好正则匹配，名称结尾太多不好匹配，放弃。

尝试二：利用显著特征，直接传统方法定位，切出来识别

        国徽就是个显著特征，利用国徽模板匹配，角度和位置就有了，然后用相对固定的比例系数乘以输入图片宽高，切出来后整个主要文字区域就有了，然后还是按比例从主区域中一块块的切，由于图片拍摄质量问题放弃。

尝试三：yolov8训练一个统一社会信用代码、企业名称位置检测

        效果还不错，先检测出位置，再裁剪出图片OCR。

效果

模型信息

Model Properties
-------------------------
author：Ultralytics
task：detect
license：AGPL-3.0
version：8.0.184
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'code', 1: 'name'}
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output0
tensor：Float[1, 6, 8400]
---------------------------------------------------------------

项目

VS2022+ framework 4.8

OpenCvSharp 4.8

Microsoft.ML.OnnxRuntime 1.16.2

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;namespace Onnx_Yolov8_Detect
{public partial class frmMain : Form{public frmMain(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";string startupPath;string classer_path;string model_path;DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;Mat image;Mat result_image;SessionOptions options;InferenceSession onnx_session;Tensor<float> input_tensor;List<NamedOnnxValue> input_ontainer;IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensor<float> result_tensors;float[] result_array;float[] factors = new float[2];Result result;DetectionResult result_pro;StringBuilder sb = new StringBuilder();private void button1_Click(object sender, EventArgs e){OpenFileDialog ofd = new OpenFileDialog();ofd.Filter = fileFilter;if (ofd.ShowDialog() != DialogResult.OK) return;pictureBox1.Image = null;pictureBox2.Image = null;textBox1.Text = "";image_path = ofd.FileName;pictureBox1.Image = new Bitmap(image_path);image = new Mat(image_path);}private void Form1_Load(object sender, EventArgs e){startupPath = Application.StartupPath + "\\model\\";model_path = startupPath + "best.onnx";classer_path = startupPath + "lable.txt";// 创建输出会话options = new SessionOptions();options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行// 创建推理模型类，读取本地模型文件onnx_session = new InferenceSession(model_path, options);// 输入Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });// 创建输入容器input_ontainer = new List<NamedOnnxValue>();}private void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}textBox1.Text = "检测中，请稍等……";pictureBox2.Image = null;Application.DoEvents();//图片缩放image = new Mat(image_path);int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);Rect roi = new Rect(0, 0, image.Cols, image.Rows);image.CopyTo(new Mat(max_image, roi));factors[0] = factors[1] = (float)(max_image_length / 640.0);//将图片转为RGB通道Mat image_rgb = new Mat();Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);Mat resize_image = new Mat();Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));//输入Tensorfor (int y = 0; y < resize_image.Height; y++){for (int x = 0; x < resize_image.Width; x++){input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;}}//将 input_tensor 放入一个输入参数的容器，并指定名称input_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));dt1 = DateTime.Now;//运行 Inference 并获取结果result_infer = onnx_session.Run(input_ontainer);dt2 = DateTime.Now;//将输出结果转为DisposableNamedOnnxValue数组results_onnxvalue = result_infer.ToArray();//读取第一个节点输出并转为Tensor数据result_tensors = results_onnxvalue[0].AsTensor<float>();result_array = result_tensors.ToArray();resize_image.Dispose();image_rgb.Dispose();result_pro = new DetectionResult(classer_path, factors, 0.8f, 0.5f);result = result_pro.process_result(result_array);result_image = result_pro.draw_result(result, image.Clone());if (!result_image.Empty()){pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());sb.Clear();sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");sb.AppendLine("------------------------------");for (int i = 0; i < result.length; i++){sb.AppendLine(string.Format("{0}：{1},({2},{3},{4},{5})", result.classes[i], result.scores[i].ToString("0.00"), result.rects[i].TopLeft.X, result.rects[i].TopLeft.Y, result.rects[i].BottomRight.X, result.rects[i].BottomRight.Y));}textBox1.Text = sb.ToString();}else{textBox1.Text = "无信息";}}private void pictureBox2_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox2.Image);}private void pictureBox1_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox1.Image);}}
}

下载

源码下载

其他

OCR识别参考  C# OpenVINO 通用OCR识别 文字识别 中文识别 服务-CSDN博客