Apziva - MonReader (Image Classifier)

Run

Run project using python src\models\train_model.py to train the models and using python src\models\predict_model.py to test them

Background:

Our company develops innovative Artificial Intelligence and Computer Vision solutions that revolutionize industries. Machines that can see: We pack our solutions in small yet intelligent devices that can be easily integrated to your existing data flow. Computer vision for everyone: Our devices can recognize faces, estimate age and gender, classify clothing types and colors, identify everyday objects and detect motion. Technical consultancy: We help you identify use cases of artificial intelligence and computer vision in your industry. Artificial intelligence is the technology of today, not the future.

MonReader is a new mobile document digitization experience for the blind, for researchers and for everyone else in need for fully automatic, highly fast and high-quality document scanning in bulk. It is composed of a mobile app and all the user needs to do is flip pages and everything is handled by MonReader: it detects page flips from low-resolution camera preview and takes a high-resolution picture of the document, recognizing its corners and crops it accordingly, and it dewarps the cropped document to obtain a bird's eye view, sharpens the contrast between the text and the background and finally recognizes the text with formatting kept intact, being further corrected by MonReader's ML powered redactor.

MonReader is a new mobile document digitalization experience for the blind, for researchers and for everyone else in need for fully automatic, highly fast and high-quality document scanning in bulk. It is composed of a mobile app and all the user needs to do is flip pages and everything is handled by MonReader: it detects page flips from low-resolution camera preview and takes a high-resolution picture of the document, recognizing its corners and crops it accordingly, and it dewarps the cropped document to obtain a bird's eye view, sharpens the contrast between the text and the background and finally recognizes the text with formatting kept intact, being further corrected by MonReader's ML powered redactor.

Data Description:

We collected page flipping video from smart phones and labelled them as flipping and not flipping.

We clipped the videos as short videos and labelled them as flipping or not flipping. The extracted frames are then saved to disk in a sequential order with the following naming structure: VideoID_FrameNumber

Goal(s):

Predict if the page is being flipped using a single image.

Success Metric(s):

• Evaluate model performance based on F1 score, the higher the better.

• Evaluate model size, the smaller the better

Project Structure

├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── LICENSE

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Project based on the cookiecutter data science project template. #cookiecutterdatascience