How to Develop a Custom Image Recognition System in FlutterFlow

In Google Cloud Console, enable the Cloud Vision API for your project. Create a service account with Vision API access and download the JSON key file. In your Cloud Function, install @google-cloud/vision and create a client with the service account credentials. The function receives a base64 image string and calls client.annotateImage() with features: LABEL_DETECTION (identifies objects/scenes) and TEXT_DETECTION (reads text in images). Return the parsed results.

1const vision = require('@google-cloud/vision');
2const client = new vision.ImageAnnotatorClient();
3
4exports.analyzeImage = async (req, res) => {
5  const { imageBase64 } = req.body;
6  const request = {
7    image: { content: imageBase64 },
8    features: [
9      { type: 'LABEL_DETECTION', maxResults: 10 },
10      { type: 'TEXT_DETECTION', maxResults: 5 },
11    ],
12  };
13  const [result] = await client.annotateImage(request);
14  const labels = result.labelAnnotations.map(l => ({
15    description: l.description,
16    score: Math.round(l.score * 100),
17  }));
18  const text = result.fullTextAnnotation?.text || '';
19  res.json({ labels, text });
20};

Create a Custom Action called imageToBase64 that takes a file path (from camera capture or gallery picker) and returns a base64 string. Read the file bytes, encode with base64Encode from dart:convert. Before encoding, resize the image to max 1024px on the longest side using the image package to reduce upload size and API costs. Large camera photos (5MB+) are slow to upload and cost more per Vision API call.

On your analysis page, add a FlutterFlowUploadButton or custom camera capture button. After the user selects an image, show it in a preview Image widget. Call the imageToBase64 Custom Action, then call the Cloud Function API with the base64 string. Show a CircularProgressIndicator during processing. On success, parse the response and display labels in a ListView — each Row shows the label description and a LinearPercentIndicator for the confidence score.

For text recognition without internet, add google_mlkit_text_recognition to Pubspec Dependencies. Create a Custom Action called recognizeText that takes an image file path, creates an InputImage, and calls TextRecognizer().processImage(). Extract recognized text blocks and return the concatenated text. This works offline and is free (no API costs), but only supports text, not object labels.

For text detection results, show the extracted text in a SelectableText widget (allows copy) or a TextField (allows editing). Add a Copy to Clipboard button using a Custom Action with Clipboard.setData(). This is useful for receipt scanning, document digitization, and business card reading. Format the text by preserving line breaks from the Vision API response.