前言

PaddleOCR 提供了基于深度學(xué)習(xí)的文本檢測、識別和方向檢測等功能。其主要推薦的 PP-OCR 算法在國內(nèi)外的企業(yè)開發(fā)者中得到廣泛應(yīng)用。在短短的幾年時間里，PP-OCR 的累計 Star 數(shù)已經(jīng)超過了32.2k，常常出現(xiàn)在 GitHub Trending 和 Paperswithcode 的日榜和月榜第一位，被認為是當前OCR領(lǐng)域最熱門的倉庫之一。

PaddleOCR 最初主打的 PP-OCR 系列模型在去年五月份推出了 v3 版本。最近，飛槳 AI 套件團隊對 PP-OCRv3 進行了全面改進，推出了重大更新版本 PP-OCRv4。這個新版本預(yù)計帶來了更先進的技術(shù)、更高的性能和更廣泛的適用性，將進一步推動OCR技術(shù)在各個領(lǐng)域的應(yīng)用。

PP-OCRv4在速度可比情況下，中文場景端到端 Hmean 指標相比于 PP-OCRv3提升4.25%，效果大幅提升。具體指標如下表所示：

測試環(huán)境：CPU 型號為 Intel Gold 6148，CPU 預(yù)測時使用 OpenVINO。

除了更新中文模型，本次升級也優(yōu)化了英文數(shù)字模型，在自有評估集上文本識別準確率提升6%，如下表所示：

同時，也對已支持的80余種語言識別模型進行了升級更新，在有評估集的四種語系識別準確率平均提升5%以上，如下表所示：

一、模型轉(zhuǎn)換

1.模型下載

從https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.7 下載要用到的模型，要下載的模型有文本檢測模型、文字方向模型、文字識別模型，我這里只下下載了文本檢測與文字識別的模型。

下載好的模型nference.pdiparams為模型參數(shù)文件，inference.pdmodel為模型結(jié)構(gòu)文件，這兩個文件在轉(zhuǎn)換onnx的時候都要用到。

2.模型轉(zhuǎn)成onnx

使用paddle2ONNX進行模型轉(zhuǎn)換，git地址：https://github.com/paddlepaddle/paddle2onnx, 下載源碼然后編譯轉(zhuǎn)換，也可以使用在線轉(zhuǎn)換的方法，如果嫌麻煩，最好使用在線的轉(zhuǎn)換方法，在線地址：https://www.paddlepaddle.org.cn/paddle/visualdl/modelconverter/x2paddle

3. onnx轉(zhuǎn)ncnn模型

這里為了之后在移動部署做準備，選擇使用NCNN做最終的模型推理，NCNN封裝了很高效的API接品，可以方便地在移動設(shè)備和嵌入式系統(tǒng)上進行神經(jīng)網(wǎng)絡(luò)的部署和推理。適用于移動設(shè)備和嵌入式設(shè)備。它被設(shè)計用于在各種硬件平臺上高效地運行神經(jīng)網(wǎng)絡(luò)推斷（inference）。NCNN主要特點包括：

1. 輕量級和高效性: NCNN被設(shè)計為一個輕量級框架，具有高度優(yōu)化的推斷性能。它的設(shè)計目標是在移動設(shè)備和嵌入式設(shè)備上實現(xiàn)高效的神經(jīng)網(wǎng)絡(luò)推理。

2. 跨平臺支持: NCNN支持多種硬件平臺，包括CPU、GPU、DSP等，并且可以在各種操作系統(tǒng)上運行，如Windows、Android、iOS、Linux等。

3. 優(yōu)化和硬件加速: NCNN對各種硬件進行了優(yōu)化，并利用硬件加速特性提高了神經(jīng)網(wǎng)絡(luò)推斷的性能。

4. 豐富的模型支持: NCNN支持各種常見的深度學(xué)習(xí)模型，如AlexNet、VGG、ResNet、MobileNet等，并且兼容一些深度學(xué)習(xí)框架導(dǎo)出的模型，Caffe、TensorFlow、ONNX等。

可以從https://github.com/Tencent/ncnn 獲取源碼進行編譯，也可以下載官方編譯好的lib進行轉(zhuǎn)換，還可以使用在線接口進行轉(zhuǎn)換。在線接地址：https://convertmodel.com/。

轉(zhuǎn)出來的模型后綴是.param和.bin文件。

二、文本檢測

文本檢測是旨在從圖像或視頻中準確地檢測和定位文本的位置和邊界框，OCR系統(tǒng)中的一個重要組成部分，它為后續(xù)的文本識別提供了定位和定界的信息。

1. 預(yù)處理：對輸入的圖像進行預(yù)處理，可能包括圖像增強、去噪、尺寸標準化等操作，以便更好地適應(yīng)文本檢測算法。

2. 文本區(qū)域檢測：使用特定的算法或模型來檢測圖像中可能包含文本的區(qū)域。常見的方法包括基于區(qū)域的方法（如基于區(qū)域的CNN（R-CNN）系列）、基于錨點的方法（如SSD和YOLO）、以及基于注意力機制的方法（如EAST、TextBoxes++等）。

3. 后處理：在獲取文本區(qū)域的初始預(yù)測結(jié)果后，可以進行后處理步驟來提高檢測的準確性和穩(wěn)定性。這可能包括非極大值抑制（NMS）來消除重疊的邊界框、邊框回歸以精細調(diào)整邊界框的位置等。

文本檢測類：

#ifndef __OCR_DBNET_H__
#define __OCR_DBNET_H__#include "base_struct.h"
#include <ncnn/net.h>
#include <vector>
#include <ncnn/cpu.h>namespace NCNNOCR
{class DbNet{public:DbNet();~DbNet() {};int read_model(std::string param_path = "data/det.param",std::string bin_path = "data/det.bin", bool use_gpu = true);bool detect(cv::Mat& src, std::vector<TextBox>& results, int _target_size = 1024);private:ncnn::Net net;const float meanValues[3] = { 0.485 * 255, 0.456 * 255, 0.406 * 255 };const float normValues[3] = { 1.0 / 0.229 / 255.0, 1.0 / 0.224 / 255.0,1.0 / 0.225 / 255.0 };float boxThresh = 0.3f;float boxScoreThresh = 0.5f;float unClipRatio = 2.0f;int target_size;};
}#endif //__OCR_DBNET_H__

類實現(xiàn)：

#include "db_net.h"
#include "tools.h"namespace NCNNOCR
{int DbNet::read_model(std::string param_path, std::string bin_path, bool use_gpu){ncnn::set_cpu_powersave(2);ncnn::set_omp_num_threads(ncnn::get_big_cpu_count());net.opt = ncnn::Option();#if NCNN_VULKANnet.opt.use_vulkan_compute = use_gpu;
#endifnet.opt.lightmode = true;net.opt.num_threads = ncnn::get_big_cpu_count();int rp = net.load_param(param_path.c_str());int rb = net.load_model(bin_path.c_str());if (rp == 0 || rb == 0){return false;}return true;}std::vector<TextBox> inline findRsBoxes(const cv::Mat& fMapMat,const cv::Mat& norfMapMat,const float boxScoreThresh,const float unClipRatio){const float minArea = 3;std::vector<TextBox> rsBoxes;rsBoxes.clear();std::vector<std::vector<cv::Point>> contours;cv::findContours(norfMapMat, contours, cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);for (int i = 0; i < contours.size(); ++i) {float minSideLen, perimeter;std::vector<cv::Point> minBox =getMinBoxes(contours[i], minSideLen, perimeter);if (minSideLen < minArea)continue;float score = boxScoreFast(fMapMat, contours[i]);if (score < boxScoreThresh)continue;//---use clipper start---std::vector<cv::Point> clipBox = unClip(minBox, perimeter, unClipRatio);std::vector<cv::Point> clipMinBox =getMinBoxes(clipBox, minSideLen, perimeter);//---use clipper end---if (minSideLen < minArea + 2)continue;for (int j = 0; j < clipMinBox.size(); ++j) {clipMinBox[j].x = (clipMinBox[j].x / 1.0);clipMinBox[j].x =(std::min)((std::max)(clipMinBox[j].x, 0), norfMapMat.cols);clipMinBox[j].y = (clipMinBox[j].y / 1.0);clipMinBox[j].y =(std::min)((std::max)(clipMinBox[j].y, 0), norfMapMat.rows);}rsBoxes.emplace_back(TextBox{ clipMinBox, score });}reverse(rsBoxes.begin(), rsBoxes.end());return rsBoxes;}bool DbNet::detect(cv::Mat& src, std::vector<TextBox>& results_, int _target_size){target_size = _target_size;int width = src.cols;int height = src.rows;int w = width;int h = height;float scale = 1.f;const int resizeMode = 0; // min = 0, max = 1if (resizeMode == 1) {if (w < h) {scale = (float)target_size / w;w = target_size;h = h * scale;}else {scale = (float)target_size / h;h = target_size;w = w * scale;}}else if (resizeMode == 0) {if (w > h) {scale = (float)target_size / w;w = target_size;h = h * scale;}else {scale = (float)target_size / h;w = w * scale;h = target_size;}}ncnn::Extractor extractor = net.create_extractor();ncnn::Mat out;cv::Size in_pad_size;int wpad = (w + 31) / 32 * 32 - w;int hpad = (h + 31) / 32 * 32 - h;ncnn::Mat in_pad_;ncnn::Mat input = ncnn::Mat::from_pixels_resize(src.data, ncnn::Mat::PIXEL_RGB, width, height, w, h);// pad to target_size rectanglencnn::copy_make_border(input, in_pad_, hpad / 2, hpad - hpad / 2, wpad / 2,wpad - wpad / 2, ncnn::BORDER_CONSTANT, 0.f);in_pad_.substract_mean_normalize(meanValues, normValues);in_pad_size = cv::Size(in_pad_.w, in_pad_.h);extractor.input("x", in_pad_);extractor.extract("sigmoid_0.tmp_0", out);//    ncnn::Mat flattened_out = out.reshape(out.w * out.h * out.c);//-----boxThresh-----cv::Mat fMapMat(in_pad_size.height, in_pad_size.width, CV_32FC1, (float*)out.data);cv::Mat norfMapMat;norfMapMat = fMapMat > boxThresh;cv::Mat element = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));cv::dilate(norfMapMat, norfMapMat, element, cv::Point(-1, -1), 1);std::vector<TextBox> results =findRsBoxes(fMapMat, norfMapMat, boxScoreThresh,unClipRatio);for (int i = 0; i < results.size(); i++) {for (int j = 0; j < results[i].boxPoint.size(); j++) {float x = float(results[i].boxPoint[j].x - (wpad / 2)) / scale;float y = float(results[i].boxPoint[j].y - (hpad / 2)) / scale;x = std::max(std::min(x, (float)(width - 1)), 0.f);y = std::max(std::min(y, (float)(height - 1)), 0.f);results[i].boxPoint[j].x = (int)x;results[i].boxPoint[j].y = (int)y;}if (abs(results[i].boxPoint[0].x - results[i].boxPoint[1].x) <= 3) {continue;}if (abs(results[i].boxPoint[0].y - results[i].boxPoint[3].y) <= 3) {continue;}results_.push_back(results[i]);}return true;}DbNet::DbNet(){}
}

檢測結(jié)果：

三、文字識別

1. OCR

文字識別是將印刷或手寫文本轉(zhuǎn)換為可文本，被廣泛應(yīng)用于各種領(lǐng)域，包括數(shù)字化檔案管理、自動化數(shù)據(jù)錄入、圖像搜索、身份驗證、自動車牌識別、票據(jù)處理、手寫文字識別。

類聲明：

#ifndef __OCR_CRNNNET_H__
#define __OCR_CRNNNET_H__#include "base_struct.h"
#include <ncnn/net.h>
#include <opencv2/opencv.hpp>
#include <vector>
#include <ncnn/cpu.h>
#include <fstream>namespace NCNNOCR
{class CrnnNet{public:CrnnNet();~CrnnNet() {};int read_model(std::string param_path = "data/ch_recv4.ncnn.param",std::string bin_path = "data/ch_recv4.ncnn.bin",std::string key_path = "data/dict_chi_sim.txt", bool use_gpu = false);int read_keys(std::string key_path);bool detect(cv::Mat& src, TextLine& result);bool detect(std::vector<cv::Mat>& src, std::vector<TextLine>& results);private:TextLine scoreToTextLine(const std::vector<float>& outputData, int h, int w);private:ncnn::Net net;const int dstHeight = 48;const int dstWidth = 320;const float meanValues[3] = { 127.5, 127.5, 127.5 };const float normValues[3] = { 1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5 };std::vector<std::string> keys;};
}#endif //__OCR_DBNET_H__

類實現(xiàn)：

#include "crnn_net.h"namespace NCNNOCR
{template<class ForwardIterator>inline static size_t argmax(ForwardIterator first, ForwardIterator last){return std::distance(first, std::max_element(first, last));}int CrnnNet::read_model(std::string param_path, std::string bin_path,std::string key_path, bool use_gpu){ncnn::set_cpu_powersave(2);ncnn::set_omp_num_threads(ncnn::get_big_cpu_count());net.opt = ncnn::Option();#if NCNN_VULKANnet.opt.use_vulkan_compute = use_gpu;
#endifnet.opt.num_threads = ncnn::get_big_cpu_count();int rp = net.load_param(param_path.c_str());int rb = net.load_model(bin_path.c_str());int rk = read_keys(key_path);if (rp == 0 || rb == 0 || rk == 0){return false;}return true;}int CrnnNet::read_keys(std::string key_path){std::ifstream in(key_path.c_str());std::string line;if (in){while (getline(in, line)){// line中不包括每行的換行符keys.push_back(line);}}else{printf("The keys.txt file was not found\n");}keys.insert(keys.begin(), "#");keys.emplace_back(" ");return keys.size();};TextLine CrnnNet::scoreToTextLine(const std::vector<float>& outputData, int h, int w){int keySize = keys.size();std::string strRes;std::vector<float> scores;int lastIndex = -1;int maxIndex;float maxValue;for (int i = 0; i < h; i++){maxIndex = 0;maxValue = -1000.f;maxIndex = int(argmax(outputData.begin() + i * w, outputData.begin() + i * w + w));maxValue = float(*std::max_element(outputData.begin() + i * w, outputData.begin() + i * w + w)); // / partition;if (maxIndex > 0 && maxIndex < keySize && (!(maxIndex == lastIndex))){/* std::cout << maxIndex << std::endl;*/scores.emplace_back(maxValue);//std::cout << keys[maxIndex] << std::endl;strRes.append(keys[maxIndex]);}lastIndex = maxIndex;}return { strRes, scores };}bool CrnnNet::detect(cv::Mat& src, TextLine& result){int resized_w = 0;float ratio = src.cols / float(src.rows);resized_w = ceil(dstHeight * ratio);cv::Size tmp = cv::Size(resized_w, dstHeight);ncnn::Mat input = ncnn::Mat::from_pixels_resize(src.data, ncnn::Mat::PIXEL_BGR2RGB,src.cols, src.rows, tmp.width, tmp.height);input.substract_mean_normalize(meanValues, normValues);ncnn::Extractor extractor = net.create_extractor();extractor.input("in0", input);ncnn::Mat out;extractor.extract("out0", out);float* floatArray = (float*)out.data;std::vector<float> outputData(floatArray, floatArray + out.h * out.w);result = scoreToTextLine(outputData, out.h, out.w);return true;}bool CrnnNet::detect(std::vector<cv::Mat>& src,std::vector<TextLine>& results){int sizeLen = src.size();// results.resize(sizeLen);for (size_t i = 0; i < sizeLen; i++){TextLine textline;if (detect(src[i], textline)){results.emplace_back(textline);}else{return false;}}return true;}CrnnNet::CrnnNet(){}
}

2.在圖像畫中文

識別后，想要比對識別的結(jié)果，可以把文字畫到當前圖像，但OpenCV沒有提供畫中文的方法，甩以要自己寫一個畫中文的方法：

#include "put_text.h"void get_string_size(HDC hDC, const char* str, int* w, int* h)
{SIZE size;GetTextExtentPoint32A(hDC, str, strlen(str), &size);if (w != 0) *w = size.cx;if (h != 0) *h = size.cy;
}void put_text_ch(Mat &dst, const char* str, Point org, Scalar color, int fontSize, const char* fn, bool italic, bool underline)
{CV_Assert(dst.data != 0 && (dst.channels() == 1 || dst.channels() == 3));int x, y, r, b;if (org.x > dst.cols || org.y > dst.rows) return;x = org.x < 0 ? -org.x : 0;y = org.y < 0 ? -org.y : 0;LOGFONTA lf;lf.lfHeight = -fontSize;lf.lfWidth = 0;lf.lfEscapement = 0;lf.lfOrientation = 0;lf.lfWeight = 5;lf.lfItalic = italic;   //斜體lf.lfUnderline = underline; //下劃線lf.lfStrikeOut = 0;lf.lfCharSet = DEFAULT_CHARSET;lf.lfOutPrecision = 0;lf.lfClipPrecision = 0;lf.lfQuality = PROOF_QUALITY;lf.lfPitchAndFamily = 0;strcpy_s(lf.lfFaceName, fn);HFONT hf = CreateFontIndirectA(&lf);HDC hDC = CreateCompatibleDC(0);HFONT hOldFont = (HFONT)SelectObject(hDC, hf);int strBaseW = 0, strBaseH = 0;int singleRow = 0;char buf[1 << 12];strcpy_s(buf, str);char *bufT[1 << 12];  // 這個用于分隔字符串后剩余的字符，可能會超出。//處理多行{int nnh = 0;int cw, ch;const char* ln = strtok_s(buf, "\n", bufT);while (ln != 0){get_string_size(hDC, ln, &cw, &ch);strBaseW = max(strBaseW, cw);strBaseH = max(strBaseH, ch);ln = strtok_s(0, "\n", bufT);nnh++;}singleRow = strBaseH;strBaseH *= nnh;}if (org.x + strBaseW < 0 || org.y + strBaseH < 0){SelectObject(hDC, hOldFont);DeleteObject(hf);DeleteObject(hDC);return;}r = org.x + strBaseW > dst.cols ? dst.cols - org.x - 1 : strBaseW - 1;b = org.y + strBaseH > dst.rows ? dst.rows - org.y - 1 : strBaseH - 1;org.x = org.x < 0 ? 0 : org.x;org.y = org.y < 0 ? 0 : org.y;BITMAPINFO bmp = { 0 };BITMAPINFOHEADER& bih = bmp.bmiHeader;int strDrawLineStep = strBaseW * 3 % 4 == 0 ? strBaseW * 3 : (strBaseW * 3 + 4 - ((strBaseW * 3) % 4));bih.biSize = sizeof(BITMAPINFOHEADER);bih.biWidth = strBaseW;bih.biHeight = strBaseH;bih.biPlanes = 1;bih.biBitCount = 24;bih.biCompression = BI_RGB;bih.biSizeImage = strBaseH * strDrawLineStep;bih.biClrUsed = 0;bih.biClrImportant = 0;void* pDibData = 0;HBITMAP hBmp = CreateDIBSection(hDC, &bmp, DIB_RGB_COLORS, &pDibData, 0, 0);CV_Assert(pDibData != 0);HBITMAP hOldBmp = (HBITMAP)SelectObject(hDC, hBmp);//color.val[2], color.val[1], color.val[0]SetTextColor(hDC, RGB(255, 255, 255));SetBkColor(hDC, 0);//SetStretchBltMode(hDC, COLORONCOLOR);strcpy_s(buf, str);const char* ln = strtok_s(buf, "\n", bufT);int outTextY = 0;while (ln != 0){TextOutA(hDC, 0, outTextY, ln, strlen(ln));outTextY += singleRow;ln = strtok_s(0, "\n", bufT);}uchar* dstData = (uchar*)dst.data;int dstStep = dst.step / sizeof(dstData[0]);unsigned char* pImg = (unsigned char*)dst.data + org.x * dst.channels() + org.y * dstStep;unsigned char* pStr = (unsigned char*)pDibData + x * 3;for (int tty = y; tty <= b; ++tty){unsigned char* subImg = pImg + (tty - y) * dstStep;unsigned char* subStr = pStr + (strBaseH - tty - 1) * strDrawLineStep;for (int ttx = x; ttx <= r; ++ttx){for (int n = 0; n < dst.channels(); ++n) {double vtxt = subStr[n] / 255.0;int cvv = vtxt * color.val[n] + (1 - vtxt) * subImg[n];subImg[n] = cvv > 255 ? 255 : (cvv < 0 ? 0 : cvv);}subStr += 3;subImg += dst.channels();}}SelectObject(hDC, hOldBmp);SelectObject(hDC, hOldFont);DeleteObject(hf);DeleteObject(hBmp);DeleteDC(hDC);
}

3.字符轉(zhuǎn)換

識別的字符屬于UTF8，在windows下，要轉(zhuǎn)成ASCII才能正常顯示不亂碼，在C++中，可以使用標準庫中的一些函數(shù)來處理字符編碼的轉(zhuǎn)換，但需要注意UTF-8和ASCII字符編碼之間的差異。因為UTF-8是一種更廣泛支持字符的編碼方式，所以在進行轉(zhuǎn)換時，需要確保要轉(zhuǎn)換的文本僅包含ASCII字符。

#include "EncodeConversion.h"
#include <Windows.h>//utf8 轉(zhuǎn) Unicode
extern std::wstring Utf8ToUnicode(const std::string& utf8string)
{int widesize = ::MultiByteToWideChar(CP_UTF8, 0, utf8string.c_str(), -1, NULL, 0);if (widesize == ERROR_NO_UNICODE_TRANSLATION){throw std::exception("Invalid UTF-8 sequence.");}if (widesize == 0){throw std::exception("Error in conversion.");}std::vector<wchar_t> resultstring(widesize);int convresult = ::MultiByteToWideChar(CP_UTF8, 0, utf8string.c_str(), -1, &resultstring[0], widesize);if (convresult != widesize){throw std::exception("La falla!");}return std::wstring(&resultstring[0]);
}//unicode 轉(zhuǎn)為 ascii
extern std::string WideByteToAcsi(std::wstring& wstrcode)
{int asciisize = ::WideCharToMultiByte(CP_OEMCP, 0, wstrcode.c_str(), -1, NULL, 0, NULL, NULL);if (asciisize == ERROR_NO_UNICODE_TRANSLATION){throw std::exception("Invalid UTF-8 sequence.");}if (asciisize == 0){throw std::exception("Error in conversion.");}std::vector<char> resultstring(asciisize);int convresult = ::WideCharToMultiByte(CP_OEMCP, 0, wstrcode.c_str(), -1, &resultstring[0], asciisize, NULL, NULL);if (convresult != asciisize){throw std::exception("La falla!");}return std::string(&resultstring[0]);
}//utf-8 轉(zhuǎn) ascii
extern std::string UTF8ToASCII(std::string& strUtf8Code)
{std::string strRet("");//先把 utf8 轉(zhuǎn)為 unicodestd::wstring wstr = Utf8ToUnicode(strUtf8Code);//最后把 unicode 轉(zhuǎn)為 asciistrRet = WideByteToAcsi(wstr);return strRet;
}//ascii 轉(zhuǎn) Unicode
extern std::wstring AcsiToWideByte(std::string& strascii)
{int widesize = MultiByteToWideChar(CP_ACP, 0, (char*)strascii.c_str(), -1, NULL, 0);if (widesize == ERROR_NO_UNICODE_TRANSLATION){throw std::exception("Invalid UTF-8 sequence.");}if (widesize == 0){throw std::exception("Error in conversion.");}std::vector<wchar_t> resultstring(widesize);int convresult = MultiByteToWideChar(CP_ACP, 0, (char*)strascii.c_str(), -1, &resultstring[0], widesize);if (convresult != widesize){throw std::exception("La falla!");}return std::wstring(&resultstring[0]);
}//Unicode 轉(zhuǎn) Utf8
extern std::string UnicodeToUtf8(const std::wstring& widestring)
{int utf8size = ::WideCharToMultiByte(CP_UTF8, 0, widestring.c_str(), -1, NULL, 0, NULL, NULL);if (utf8size == 0){throw std::exception("Error in conversion.");}std::vector<char> resultstring(utf8size);int convresult = ::WideCharToMultiByte(CP_UTF8, 0, widestring.c_str(), -1, &resultstring[0], utf8size, NULL, NULL);if (convresult != utf8size){throw std::exception("La falla!");}return std::string(&resultstring[0]);
}//ascii 轉(zhuǎn) Utf8
extern std::string ASCIIToUTF8(std::string& strAsciiCode)
{std::string strRet("");//先把 ascii 轉(zhuǎn)為 unicodestd::wstring wstr = AcsiToWideByte(strAsciiCode);//最后把 unicode 轉(zhuǎn)為 utf8strRet = UnicodeToUtf8(wstr);return strRet;
}

三、整體測試

#include <iostream>
#include "crnn_net.h"
#include "db_net.h"
#include "tools.h"
#include "put_text.h"
#include "EncodeConversion.h"int main() 
{NCNNOCR::DbNet det_net;NCNNOCR::CrnnNet rec_net;rec_net.read_model();det_net.read_model();cv::Mat img = cv::imread("235.jpg");if (img.empty()){std::cout << "empty" << std::endl;return 0;}cv::Mat drawImg = img.clone();std::vector< NCNNOCR::TextBox> boxResult;std::vector< NCNNOCR::TextLine> recResult;det_net.detect(img, boxResult,2560);recResult.resize(boxResult.size());for (size_t i = 0; i < boxResult.size(); i++) {cv::Mat partImg = NCNNOCR::getRotateCropImage(img, boxResult[i].boxPoint);rec_net.detect(partImg, recResult[i]);cv::polylines(drawImg, boxResult[i].boxPoint, true, cv::Scalar(0,0,255),4);std::string text = UTF8ToASCII(recResult.at(i).text);std::cout << text << std::endl;if (text.empty()){continue;}put_text_ch(drawImg, text.c_str(), boxResult[i].boxPoint[0], cv::Scalar(0, 0, 255), 80);}cv::namedWindow("result", 0);cv::imshow("result", drawImg);cv::waitKey();return 0;
}