LabelReader is a general Machine Learning-based solution to identifying labels in a picture.

A common problem is using OCR on a simple picture. But what if that picture is complicated, has many items, and words that aren't important to the user? LabelReader finds the important label in the picture, crops it out, rotates it, and then reads the label to pinpoint the object you're looking for.

The identifier's approach is straightforward:

1. Determine if a nameplate/asset is in the picture
2. Identify where that nameplate is
3. Crop out the relevant asset
4. Rotate the cropped picture so the text is readable
5. Read characters in the cropped picture
6. Find the relevant information in a database and present it to the user

LabelReader uses the Yolov3 algorithm for object detection. The user can choose between the following to interact with the algorithm:

• Darknet (Fast, C Implementation)
• Keras-Yolov3 (Python Implementation)

This repository contains a model that has been trained on labels for headphones, and will need to be tuned for custom images. For Optical Character Recognition, LabelReader sends the processed images to Azure Cognitive Services. Users need to create an account with Cognitive Services Vision to use the model. Since it takes a few seconds to send and receive the request, LabelReader supports an alternative library, Tesseract for faster OCR.

The repository contains another model, RotNet to detect how much to rotate the image. This should work for most products, but may need to be trained to suit your needs.

LabelReader can run on Docker. It is recommended to install Docker and use the base image, continuumio/miniconda3:

docker pull continuumio/miniconda3
docker run -i -t continuumio/miniconda3 /bin/bash
apt update

Then, clone the repository:

git clone https://github.com/ecthros/labelReader
cd labelReader

To install necessary dependencies, run:

./install.sh

This script will install necessary components and set up LabelReader to run. Once finished, run:

python labelReader.py [-k/-d] [-c/-t]

Make sure to specify if you want Keras or Darknet to classify, and Cognitive Services or Tesseract for OCR.

This nameplate identifier can be adapted for many causes. Identifying and analyzing parts of a picture is a very common problem, and this code is meant to be easily extendable. Simply add your own classes, extending the abstract classes given, or train your own model with the steps above.

Many users might want to create a REST endpoint on Azure. This code is also included in this repository. Simply push your docker container to Docker Hub or Azure Container Storage, extending what is written, and follow the following steps:

• Make sure your container automatically launches the web app locally
  • The endpoint will launch at /api/v1.0/image
• Navigate to Azure and log in
• Press the green "Create New Item" button
• Select "Web App"
• Enter the App name, subscription, and resource group
• Select "Docker" for the OS
• Select "Container Settings" and fill in the information for your container
• Create the container. Note that the default web app does not have enough RAM to run most ML models, and you may need to update your plan's App service pricing tier.

• Training can take several hours to complete, even with an excellent GPU.
• There are many ways to train the classifier, but Darknet is easy to use.
  • Follow the steps to train here.
  • You will need approximately a hundred classified images, in various environments and lightings, to train the model.
• Labeling with VoTT is much easier than anything else I have found.
  • VoTT also creates the cfg, data, and folders for you.
• Make sure your images are of the same aspect ratio, since Darknet will change the size to a fixed image (or, just change this parameter in Darknet)