Advanced EAST

Abstract

1. Introduction

AdvancedEAST is an algorithm used for Scene image text detect, which is primarily based on EAST:An Efficient and Accurate Scene Text Detector, and the significant improvement was also made, which make long text predictions more accurate. If this project is helpful to you, welcome to star. And if you have any problem, please contact me.

In my experiments, AdvancedEast has obtained much better prediction accuracy then East, especially on long text. Since East calculates final vertexes coordinates with weighted mean values of predicted vertexes coordinates of all pixels. It is too difficult to predict the 2 vertexes from the other side of the quadrangle. See East limitations picked from original paper bellow.

see 'demo results' part bellow, and pay attention to the activation image, which starts with yellow grids, and ends with green grids.)

2. Objective

AdvancedEAST is an algorithm used for Scene image text detect, which is primarily based on EAST, and the significant improvement was also made, which make long text predictions more accurate.https://github.com/huoyijie/raspberrypi-car

This project demonstrates how Python, Keras, TensorFlow can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/huoyijie/AdvancedEAST.git
cd AdvancedEAST

1. python 3.6.3+
2. tensorflow-gpu 1.5.0+(or tensorflow 1.5.0+)
3. keras 2.1.4+
4. numpy 1.14.1+
5. tqdm 4.19.7+

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by huoyijie and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.