Geriausia „JavaScript“ OCR API optiniam simbolių atpažinimui

Įdiekite „Aspose.OCR“, skirtą „JavaScript“ per C++ per npm

 NuGet\Install-Package Aspose.Ocr.Cpp -Version 23.8.0 

Biblioteką galite atsisiųsti tiesiogiai iš Aspose.OCR produkto puslapio

Kaip atlikti teksto ištraukimą iš vaizdų naudojant „JavaScript“ API?

const express = require('express');
const multer = require('multer');  // For handling file uploads
const child_process = require('child_process');

const app = express();
const port = 3000;

// Configure multer for handling file uploads
const storage = multer.memoryStorage();
const upload = multer({ storage: storage });

app.post('/process-image', upload.single('image'), (req, res) => {
    // Save the uploaded image to a file (you might need additional processing here)
    const imageBuffer = req.file.buffer;
    const fs = require('fs');
    fs.writeFileSync('input.jpg', imageBuffer);

    // Execute the C++ backend
    const child = child_process.spawn('./your_cpp_program', []);

    // Capture the output from the C++ backend
    let extractedText = '';
    child.stdout.on('data', (data) => {
        extractedText += data.toString();
    });

    // When the C++ process exits
    child.on('close', (code) => {
        if (code === 0) {
            res.send({ text: extractedText });
        } else {
            res.status(500).send({ error: 'OCR processing failed' });
        }
    });
});

app.listen(port, () => {
    console.log(`Server listening at http://localhost:${port}`);
});

Kaip atpažinti pasirinktą vaizdo sritį naudojant JavaScript API?

document.getElementById('process-button').addEventListener('click', () => {
    const selectedArea = {
        x: 100,  // Define the selected area's coordinates (x, y, width, height)
        y: 100,
        width: 200,
        height: 100,
    };

    const imageBlob = captureSelectedAreaAsBlob(selectedArea);  // Implement this function to capture the selected area as an image blob

    const formData = new FormData();
    formData.append('image', imageBlob);

    fetch('/api/ocr/recognize-selected-area', {
        method: 'POST',
        body: formData,
        headers: {
            'Accept': 'application/json',
        },
    })
    .then(response => response.json())
    .then(data => {
        // Handle the recognized text response
        console.log(data.text);
    })
    .catch(error => {
        console.error(error);
    });
});