Top 10 Flutter Machine Learning, OCR, and Computer Vision Packages
What are machine learning, OCR, and computer vision?
Machine Learning:
Machine learning (ML) is a subfield of computer science and artificial intelligence (AI) that focuses on using data and algorithms to make AI more accurate over time by mimicking human learning.
OCR:
The method used to transform a text image into a machine-readable text format is called optical character recognition, or OCR. Your computer saves the scan as an image file, for example, if you scan a form or a receipt. The words in the image file cannot be edited, searched for, or counted using a text editor. Nonetheless, the picture can be transformed into a text document with its contents saved as text data using OCR.
Computer Vision:
Computer vision is a branch of artificial intelligence (AI) that trains computers and systems to recognize and understand meaningful information from digital photos, videos, and other visual inputs. When it detects flaws or problems, it can then recommend actions or take action. It does this by using machine learning and neural networks.
Top 10 Flutter Machine Learning, OCR, and Computer Vision Packages:
google_ml_kit:
All the functions described below are included in the google_ml_kit umbrella plugin, which combines all the functionality into one. Using google_ml_kit will result in your app being somewhat larger because it will include all of the plugins listed below along with their corresponding dependencies. Instead of adding google_ml_kit to your yaml, we advise adding just the plugin for the feature you wish to use.
In a production application, do not use google_ml_kit; instead, stick to the plugin(s) for the functionalities mentioned below.
Features
Vision APIs:
- Barcode Scanning
- Face Detection
- Face Mesh Detection
- Text Recognition V2
- Image Labeling
- Object Detection and Tracking
- Digital Ink Recognition
- Pose Detection
- Selfie Segmentation
- Subject Segmentation
- Document Scanner
Natural Language APIs
- Language Identification
- On-Device Translation
- Smart Reply
- Entity Extraction
tflite_flutter:
This repository is an outstanding Amish Garg’s TensorFlow managed fork of the tflite_flutter_plugin project. Supporting the Flutter community in developing machine-learning-powered apps using the TensorFlow Lite framework is the aim of this project.
The TensorFlow Lite Flutter plugin offers a quick and adaptable way to use the TensorFlow Lite interpreter and carry out inference. The TFLite Java and Swift APIs are comparable to this API. Its low latency and efficiency stem from its direct binding to the TFLite C API. provides support for NNAPI, GPU delegates for Android, Metal and CoreML delegates for iOS, and the XNNPack delegate for desktop platforms for acceleration.
Key Features:
- Multi-platform Support for Android and iOS
- Flexibility to use any TFLite Model.
- Acceleration using multi-threading.
- Similar structure as TensorFlow Lite Java API.
- Inference speeds close to native Android Apps built using the Java API.
- Run inference in different isolates to prevent jank in UI thread.
edge_detection:
A Flutter plugin for detecting rectangles, corners, edges of objects, and scanning paper. It returns the path of the cropped image and permits cropping of the identified item image.
Check our edge_detection on pub.dev.
flutter_tesseract_ocr:
This plugin is based on Tesseract OCR 4 This plugin uses Tesseract4Android and SwiftyTesseract.
Tesseract is slower than ml_kit. Consider whether you should use Tesseract.
Check out flutter_tesseract_ocr on pub.dev.
flutter_scalable_ocr:
Google ML kit Text Recognition is a wrapper on the Flutter scalable OCR package. It addresses two frequent issues: narrowing down the camera viewport and retrieving data only from a portion of the camera. Here are some gifs from an example project, and you can check out the Exchange Rate Scanner app to see how it functions in real-world scenarios.
Check out flutter_scalable_ocr on pub.dev.
ml_algo:
The main purpose of the library is to give native Dart implementation of machine learning algorithms to those who are interested both in Dart language and data science. This library aims at Dart VM and Flutter, it’s impossible to use it in web applications.
Check out ml_algo on pub.dev.
google_vision:
Native Dart package that allows applications to incorporate Google Vision features such as face, logo, and landmark detection, image labeling, optical character recognition (OCR), and explicit content detection.
Check out google_vision on pub.dev.
face_camera:
A Flutter camera plugin that detects faces in real-time.
Checkout face_camera package on pub.dev
pytorch_lite:
This package is to help run Pytorch Lite model classification and YoloV5 and YoloV8.
Checkout pytorch_lite package on pub.dev
flutter_vision:
A Flutter plugin that uses TensorFlow Lite 2.x to manage Yolov5, Yolov8, and Tesseract v5 access. On Android, provide object identification, segmentation, and OCR. Not yet updated; development is in progress for iOS.
Conclusion:
In this post, we explored the top 10 Flutter packages for integrating machine learning, optical character recognition (OCR), and computer vision functionalities into your Flutter applications. These packages offer a wide range of capabilities, from barcode scanning and text recognition to object detection and image labeling.
Whether you’re a developer looking to add AI features to your app or an enthusiast interested in exploring the possibilities of Flutter with machine learning, these packages provide powerful tools to enhance your projects.
FAQ
For text recognition, you can choose between google_ml_kit and flutter_tesseract_ocr based on your specific requirements. If you prioritize speed and efficiency, google_ml_kit might be the better option. However, if you need more flexibility or compatibility with Tesseract OCR, flutter_tesseract_ocr could be the right choice.
To integrate object detection, you can use packages like google_ml_kit, flutter_vision, or edge_detection. These packages offer functionalities such as detecting objects, scanning paper documents, and identifying faces in real-time.
Yes, most of the packages mentioned in this post offer support for both Android and iOS platforms. However, it’s essential to check the documentation and compatibility of each package to ensure smooth integration into your Flutter app on both platforms.
The tflite_flutter package provides support for TensorFlow Lite models in Flutter applications. It offers multi-platform support for Android and iOS, flexibility in model usage, and acceleration using multi-threading, making it a versatile choice for integrating TensorFlow Lite models into Flutter projects.
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