updated README

README.md

@@ -1,30 +1,41 @@
 # License Plate Detection
+
 This project is done as a Final Year Project required to complete a Bachelor's degree in Electronics Engineering. The code is based on Adrian Rosebrock's (@jrosebr1) PyImageSearch post [**OpenCV: Automatic License/Number Plate Recognition (ANPR) with Python**](https://www.pyimagesearch.com/2020/09/21/opencv-automatic-license-number-plate-recognition-anpr-with-python/), edited to be able to detect Malaysian license plates which typically has white texts on black background plate. I recommend reading through his blogpost to get a general idea of how the image processing pipeline works.
 
 The project aims to:
-* Automatically detects either Malaysian license plates or other oversea license plates
-* Compare Scharr operator, Canny edge detector, and edge-less approach to detect license plates
+
+- Automatically detects either Malaysian license plates or other oversea license plates
+- Compare Scharr operator, Canny edge detector, and edge-less approach to detect license plates
 
 ## Prerequisites
-The project code utilizes the following library:
-* [Python](https://www.python.org/) v3.8.6
-* [OpenCV](https://opencv.org/) v4.4.0.46
-* [imutils](https://github.com/jrosebr1/imutils) v0.5.3
-* [scikit-image](https://scikit-image.org/) v0.17.2
-* [pytesseract](https://github.com/madmaze/pytesseract) v0.3.6
-* [Tesseract-OCR](https://tesseract-ocr.github.io/tessdoc/) v5.0.0 (alpha). (This is NOT installed through pip but with the Windows installer)
+
+The project code utilizes the following tools:
+
+- [Docker](https://www.docker.com/)
+- [Python](https://www.python.org/) v3.8.6
+- [OpenCV](https://opencv.org/) v4.4.0.46
+- [imutils](https://github.com/jrosebr1/imutils) v0.5.3
+- [scikit-image](https://scikit-image.org/) v0.17.2
+- [pytesseract](https://github.com/madmaze/pytesseract) v0.3.6
+- [Tesseract-OCR](https://tesseract-ocr.github.io/tessdoc/) v5.0.0 (alpha)
 
 ## How it works
+
 1. The project has 2 modules which is the driver and the class module named **anprdriver.py** and **anprclass.py** respectively. The driver module contains the script which the user should execute through Python while providing the arguments whereas the class module contains the three detection algorithm (Sobel, Canny, and edge-less) and other functions including saving results and workflow debug display.
 2. The driver should be executed in a terminal by using Python (`$ python anprdriver.py ...`). The arguments which could be appended are (arguments in bold are required):
    - **`-i` (input)** - path to input directory of images
    - `-c` (clear border) - should the script clear border pixels before performing OCR (default is -1, set to 1 to enable)
    - `-p` (PSM) - default tesseract page segmentation mode for license plate OCR (default is 7)
    - `-d` (debug) - should additional visualizations be shown (default is -1, set to 1 to enable)
    - `-a` (algorithm) - specify which algorithm to use (default is 1, 1 for Scharr, 2 for Canny, 3 for edge-less)
-   - `-s` (save) - whether to save the results or not (default is -1, *disable debug* and set to 1 to enable)
+   - `-s` (save) - whether to save the results or not (default is -1, _disable debug_ and set to 1 to enable)
    - `-m` (morphology) - specify morphological operation (default is 1, 1 for black hat which is better for black text on white plates, 2 for top hat which is better for the opposite)
 3. The repository includes sample images of license plates in `malaysian` and `overseas` folder. Results are saved inside `result_canny_*`, `result_sobel_*`, and `result_edgeless_*` folders respectively where `*` is either `malaysian` or `overseas`.
 
 ## Typical usage
-The script can be typically run by typing `python anprdriver.py -i /path/to/images/`. Path to the images can be written as `-i ./malaysian` for example. While only -i argument is necessary, be sure to choose the correct algorithm (-a) and morphological operation (-m) as needed.
+
+Build the Docker image `docker build -t anpr .` first then run in interactive mode `docker run -it anpr`.
+
+The script can then be typically run by typing `python3 anprdriver.py -i /path/to/images/` in the container terminal. Path to the images can be written as `-i /malaysian` for example. While only -i argument is necessary, be sure to choose the correct algorithm (-a) and morphological operation (-m) as needed.
+
+You can also include your own source image by modifying the Dockerfile.