Build handwriting number recognition app using Flutter and Tensorflow. Concetto Labs has a good experience number recognition flutter developer.

1. A Guide to Building a Handwriting Number
Recognition App Using Flutter and
Tensorflow

2. Flutter and Tensorflow are rapidly becoming one of the most popular
mobile app development tools in the market with businesses looking
to hire flutter app developers for their custom app requirements.
Any up and coming flutter app development company is looking
to constantly innovate and experiment with the tech to progress.
Flutter app development is now expanding into the arena of machine
learning where minor projects are being created based on machine
learning. One such experimental project that you can work on is
developing a handwriting recognizer app using Flutter and a machine
learning tool Tensorflow.

3. Here’s how you can get started:
1. Develop the machine learning model using
Tensorflow
This model will help the device recognize the number through its
shape and match it with the relevant digit is drawn using machine
learning. It will provide instructions to the app to display the drawn
digit in the form of an image.

4. 2. Develop the flutter mobile application for input based on
the Tensorflow model
The second step is to develop the Flutter mobile app where
1. Users can use a canvas for drawing up the desired numbers
2. The drawn image will be matched with the relevant image as
recognized by the device through the Tensorflow module

5. Begin with the Tensorflow Model

6. Step 1- Get Started
For getting started with Tensorflow, you can either run the model
locally using Python or use a notebook such as Jupiter, Colab by
Google or Azure Notebooks by Microsoft.
Pro-tip: Create an isolated virtual environment to run this project so
that it doesn’t harm the other projects in case anything goes wrong.
In case you decide to run with Python, this is the code you would
need to use to install Tensorflow Post which, you would need to
create a defined project structure to store your model in.

7. In the case of a notebook, you can start directly from the point of
importing Tensorflow.
Step 2- Load Dataset for Number Recognition
You would require a dataset that would translate the hand-written
numbers into an image format. Tensorflow already has preloaded
datasets to do just this. You can load the MINST dataset from
Tensorflow which stores numbers in 28X28 pixel format where the
images are 20X20 pixels with 4 pixels of padding on each side. The
images are greyscaled.

8. Once your dataset is loaded, you can assign the value to each
image. There are 256 values as there are colors. Generalize these
values whereby the input is directly translated into values from 0 to
9 to make space for more modification in the future.
Step 3- Compile and Train
The next step is to compile your model and train it for maximum
accuracy. A callback must be coded into the model to stop the
training process for the model once this accuracy is achieved.

9. The final code for your Tensorflow model must look something
like this:
The Flutter App Development
The next step for your flutter app developers is to create a
container app which will work with the model.
The flutter app development process is divided into two
parts
Importing the model and making it compatible with the app

10. Developing a finger painting canvas for hand-written input of numbers
For importing the model, create your custom flutter app project and
clean the main.dart file to start anew.
You must create another file where your custom code can work aside
from the lib/main.dart file. This file will then be imported on to your
project using the ‘home’ parameter. You can name it
recognizer_screen.dart.

11. Once your new project is all set up, you will need to import the
Tensorflow model and the Tensorflow library by modifying the
pubspec.yaml file. Along with this, you will also need to create a
text file with digits from 0 to 9 which will be associated with the
output of the file.
Once everything is set, you need to declare your assets within your
pubsec.yaml files. You can either declare all your assets together or
only select and insert the assets you wish to utilize.

12. Developing the App Layout and the Canvas
Once your model is set up, you need to define an app layout with a
header and footer in the form of a flex container which is set to
1. The size of the canvas where the numbers will be finger painted
will remain fixed.
For developing the canvas, you will require three flutter tools
for painting which are:

13. ● Custom Paint
● Custom Painter
● Canvas Widget
Canvas widget will provide a canvas for the input while CustomPainter
helps the widget to run paint commands.
Create a subclass called drawing_painter.dart for your CustomPainter
widget to include your custom code. You will be further required to
import the flutter/material. Art in order to utilize any colors within the
file.

14. To wrap it up, the last two things that you must define is the canvas
area so that the user cannot draw outside it and highlight the
painting area by decorating it.

15. Flutter app developers now have all the tools in place to pass on
the image form the canvas to your machine learning model for it to
predict the number in the form of an image.
In case you do not feel confident enough to venture out on your
own, the next update on the working of the app will be coming soon!

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