neural-network-digital-counter-readout
Training and using a neural network to readout the value of a digital counter - example including small node server for demonstration
The readout is used in a water meter measurement system. An overview can be found here: https://github.com/jomjol/water-meter-measurement-system
13.3.0 Current Version (2021-11-23)
- New images for digits
- BMP as input for the rescaling script is now also supported
13.2.0 Current Version (2021-10-29)
- New images (7-segment LCD, new analog types)
- Changed convolutional layer 2 & 3 to RELU activation function
13.1.0 Current Version (2021-10-06)
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New images (minor change)
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License change (remove MIT license, remark see below)
ATTENTION: LICENSE CHANGE - removal of MIT License.
- Currently no licence published - copyright belongs to author
- If you are interested in a commercial usage or dedicated versions please contact the developer
13.0.0 Current Version - (2021-09-12)
- New type of digits (green LCD)
12.1.0 Current Version - (2021-09-10)
- Adaption of augmentation range (reduction)
- Additional images, new type of digits
12.0.0 Current Version - (2021-08-01)
- Save classification in file name instead of subdirectory
- Additional images, new type of digits
Overview older Versions
Problem to solve
An image of a number between 0 and 9 should be recognized and converted to the corresponding digital value. One complicating issue is, that as the digits are comming from a rolling counter number, there are some pictures, which are not ambigious. In this case the result should be a "NaN", indicating, that this is not a full number. To solve this problem a neural network approach is used.
The rolling counter meter looks like following:
The image is sliced into individual pictures, which are analysed by a neural network.
Neural Network Approach
Convolutional Neural Networks (CNN) are very promiment in image processing. Especially in classification of image content, e.g. identify objects (dog, cat, car, bike, ...) or classify hand written letters.
Here a classic approach is used to classify the picture into 11 output classes representing the 10 digits from 0 to 9 and the "NaN".
| Picture | Value | Picture | Value | Picture | Value | Picture | Value |
|---|---|---|---|---|---|---|---|
 |
2 |  |
6 |  |
9 |  |
NaN |
Labeled Training Data
The images are coming from a camera system described elsewhere (Overview, HW, SW). One major effort is to label the pictures with the target value. The labeling is done by hand. For each digit about 150 images are collected. For the "NaN" category about 3700 images were taken. The picture are rescaled to 32x20 pixels with RGB color (3 channels).
The resized pictures as well as the original pictures (zipped in file "data_raw_all.zip") are included in the dataset. The pictures are stored in a subfolder for each digit (and NaN).
The criteria for "good" and "bad" images are described here: Labeling-Criteria.md
Training the network
The training is done using Keras in a python environment. For training purpuses the code is documented in Jupyter notebooks. The environment is setup using Ananconda with Python 3.7[1].
The training is descibed in detail in an Jyupither Notebook: How to Train the Network.
The trained network is stored in the Keras H5-format and used as an input for a simple usage in the main project for a water meter readout: https://github.com/jomjol/water-meter-system-complete
Hopefully you have fun with neural networks and find this useful.
Any questions, hints, improvements are very welcome through the GitHub channel
Best regards,
jomjol
[1]: The following book is found very useful for background, basic setting and different approaches:
Mattheiu Deru and Alassane Ndiaye: Deep Learning with TensorFlow, Keras, und Tensorflow.js