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Recognition of handwritten flowcharts with CNNs

Recognition of handwritten flowcharts using convolutional neural networks to recognize the digital flowchart

Overview

The pipeline implemented in order to solve the problem of the recognition of handwritten and computer generator flowcharts uses image preprocessing; the input image is sent to the shape-connector detector. moreover, on the flow for shapes and connectors, it uses unsharp masking and a model that is called Faster R-CNN with backbone VGG-16. I've fine-tuned the model by adjusting its hyperparameters. The existing dataset, which primarily contained handwritten samples, has been augmented with a newly curated computer-based dataset, and the model now performs optimally.

How to set up for testing detections

  1. Create a virtual environment with Conda with name 'tt' and install all libraries required to perform this project, which is separately available in requriment.sh
  2. Download / clone this repo.
  3. Shapes-connectors model:
    • Pre-trained model is already available in the directory.

Usage

  1. Please, activate your Conda enviroment.
  2. Move to inside repository folder, example: $ cd handwritten-flowchart-with-cnn
  3. Type: $ python3 handler.py
  4. Select model flowchart_3b_model.hdf5
  5. Use the "Recognize flowchart" option to test detections with handwritten or computer generated flowcharts.

Some examples of the results

1. Handwritten flowchart recognition:

image

2. Computer-generated flowchart recognition:

image

Extra

Would you like to download the dataset?

citataion:

  • Author: ISC UPIIZ students
  • Title: Flowchart 3b
  • Version: 3.0
  • Date: May 2020.
  • Editors: Onder F. Campos and David Betancourt.
  • Publisher Location: Zacatecas, Mexico.
  • Electronic Retrieval Location: https://www.kaggle.com/davbetm/flowchart-3b