Layoutparser

**Improvements**:
- Supports lazy loading for the Detectron2 module. Now the dependency for Detectron2 will be requested only when you explicitly create a `Detectron2LayoutModel` object. This might be helpful for using the plain `layoutparser` library without installing the Detectron2 module.

**New models**:
- Incorporated a pre-trained model based on the [NewspaperNavigator](https://news-navigator.labs.loc.gov) dataset: `lp://NewspaperNavigator/faster_rcnn_R_50_FPN_3x/config`

**Fixes**:
- Corrected a bug in visualization that might overwrite original the image

In this version, we released a new model for publaynet and made several improvements:

1. We released the `mask_rcnn_X_101_32x8d_FPN_3x` model trained on the `publaynet` dataset. Note: it's been trained on the full training set (while others are only trained on the validation set), and you could expect a 15% performance improvement based on this new model.
2. We improved the support for PIL images for both layout modeling and visualization
3. We improved the Default Language Settings for the Tesseract OCR model

Fixes

- Fixed a bug that could cause errors in loading Prima Models

Updates

- Update the prima MASK RCNN model with higher accuracy, and listed detailed evaluation reports.

layoutparser` now supports the following functionalities:

- **Coordinate system**:
- Supports the 3 basic coordinate system and their geometric relationships
- Supports the TextBlook and Layout system for convenient coordinate and text processing

- **OCR System**:
- Supports OCR based on Google Cloud Vision and Tesseract API.

- **Layout Modeling**:
- Supports using pre-trained Deep Learning models for layout object detection using Detection2

- **Visualization**:
- Supports highly-customizable presentation of the box coordinates and text in the detected layout