This project is still ongoing and this repo may be updated irregularly, I developed a web app for the UIED in http://uied.online

1. UIED: a hybrid tool for GUI element detection

2. Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

The repo has been upgraded with Google OCR for GUI text detection, to use the original version in our paper (using EAST as text detector), check the relase v2.3 and download the pre-trained model in this link.

UI Element Detection (UIED) is an old-fashioned computer vision (CV) based element detection approach for graphic user interface.

The input of UIED could be various UI image, such as mobile app or web page screenshot, UI design drawn by Photoshop or Sketch, and even some hand-drawn UI design. Then the approach detects and classifies text and graphic UI elements, and exports the detection result as JSON file for future application.

UIED comprises two parts to detect UI text and graphic elements, such as button, image and input bar.

• For text, it leverages Google OCR to perfrom detection.

• For graphical elements, it uses old-fashioned CV approaches to locate the elements and a CNN classifier to achieve classification.

UIED is highly customizable, you can replace both parts by your choice (e.g. other text detection approaches). Unlike black-box end-to-end deep learning approach, you can revise the algorithms in the non-text detection and merging (partially or entirely) easily to fit your task.

• Python 3.5
• Opencv 3.4.2
• Pandas

The new version of UIED equipped with Google OCR is easy to deploy and no pre-trained model is needed. Simply donwload the repo along with the dependencies.

Please replace the Google OCR key at detect_text/ocr.py line 28 with your own (apply in Google website).

To test your own image(s):

• To test single image, change input_path_img in run_single.py to your input image and the results will be output to output_root.
• To test mutiple images, change input_img_root in run_batch.py to your input directory and the results will be output to output_root.
• To adjust the parameters lively, using run_testing.py

Note: The best set of parameters vary for different types of GUI image (Mobile App, Web, PC). I highly recommend to first play with the run_testing.py to pick a good set of parameters for your data.

cnn/

• Used to train classifier for graphic UI elements
• Set path of the CNN classification model

config/

• Set data paths
• Set parameters for graphic elements detection

data/

• Input UI images and output detection results

detect_compo/

• Non-text GUI component detection

detect_text/

• GUI text detection using Google OCR

detect_merge/

• Merge the detection results of non-text and text GUI elements

The major detection algorithms are in detect_compo/, detect_text/ and detect_merge/

GUI element detection result for web screenshot