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https://github.com/bertsky/ocrd_detectron2

OCR-D wrapper for detectron2 based segmentation models
https://github.com/bertsky/ocrd_detectron2
dla ocr-d olr
Last synced: 13 days ago
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OCR-D wrapper for detectron2 based segmentation models
Host: GitHub
URL: https://github.com/bertsky/ocrd_detectron2
Owner: bertsky
Created: 2022-01-21T00:40:17.000Z (almost 3 years ago)
Default Branch: master
Last Pushed: 2024-10-01T19:58:39.000Z (about 1 month ago)
Last Synced: 2024-10-16T00:37:02.404Z (29 days ago)
Topics: dla, ocr-d, olr
Language: Python
Homepage:
Size: 1.28 GB
Stars: 16
Watchers: 4
Forks: 5
Open Issues: 6
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
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README

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# ocrd_detectron2

    OCR-D wrapper for detectron2 based segmentation models

  * [Introduction](#introduction)

  * [Installation](#installation)

  * [Usage](#usage)

     * [OCR-D processor interface ocrd-detectron2-segment](#ocr-d-processor-interface-ocrd-detectron2-segment)

  * [Models](#models)

     * [TableBank](#tablebank)

     * [PubLayNet](#publaynet)

     * [PubLayNet](#publaynet-1)

     * [LayoutParser](#layoutparser)

     * [DocBank](#docbank)

  * [Testing](#testing)

     * [Test results](#test-results)

## Introduction

This offers [OCR-D](https://ocr-d.de) compliant [workspace processors](https://ocr-d.de/en/spec/cli) for document layout analysis with models trained on [Detectron2](https://github.com/facebookresearch/detectron2), which implements [Faster R-CNN](https://arxiv.org/abs/1506.01497), [Mask R-CNN](https://arxiv.org/abs/1703.06870), [Cascade R-CNN](https://arxiv.org/abs/1712.00726), [Feature Pyramid Networks](https://arxiv.org/abs/1612.03144) and [Panoptic Segmentation](https://arxiv.org/abs/1801.00868), among others.

In trying to cover a broad range of third-party models, a few sacrifices have to be made: Deployment of [models](#models) may be difficult, and needs configuration. Class labels (really [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) region types) must be provided. The code itself tries to cope with panoptic and instance segmentation models (with or without masks).

Only meant for (coarse) page segmentation into regions – no text lines, no reading order, no orientation.

## Installation

Create and activate a [virtual environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) as usual.

To install Python dependencies:

    make deps

Which is the equivalent of:

    pip install -r requirements.txt -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html # for CUDA 11.3

    pip install -r requirements.txt -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.10/index.html # for CPU only

To install this module, then do:

    make install

Which is the equivalent of:

    pip install .

**Alternatively**, you can use the provided **Docker image** (either from [Github Container Registry](https://github.com/users/bertsky/packages/container/package/ocrd_detectron2) or from [Dockerhub](https://hub.docker.com/r/bertsky/ocrd_detectron2)):

    docker pull bertsky/ocrd_detectron2

    # or

    docker pull ghcr.io/bertsky/ocrd_detectron2

## Usage

### [OCR-D processor](https://ocr-d.de/en/spec/cli) interface `ocrd-detectron2-segment`

To be used with [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) documents in an [OCR-D](https://ocr-d.de/en/about) annotation workflow.

```

Usage: ocrd-detectron2-segment [OPTIONS]

  Detect regions with Detectron2 models

  > Use detectron2 to segment each page into regions.

  > Open and deserialize PAGE input files and their respective images.

  > Fetch a raw and a binarized image for the page frame (possibly

  > cropped and deskewed).

  > Feed the raw image into the detectron2 predictor that has been used

  > to load the given model. Then, depending on the model capabilities

  > (whether it can do panoptic segmentation or only instance

  > segmentation, whether the latter can do masks or only bounding

  > boxes), post-process the predictions:

  > - panoptic segmentation: take the provided segment label map, and

  >   apply the segment to class label map,

  > - instance segmentation: find an optimal non-overlapping set (flat

  >   map) of instances via non-maximum suppression,

  > - both: avoid overlapping pre-existing top-level regions (incremental

  >   segmentation).

  > Then extend / shrink the surviving masks to fully include / exclude

  > connected components in the foreground that are on the boundary.

  > (This describes the steps when ``postprocessing`` is `full`. A value

  > of `only-nms` will omit the morphological extension/shrinking, while

  > `only-morph` will omit the non-maximum suppression, and `none` will

  > skip all postprocessing.)

  > Finally, find the convex hull polygon for each region, and map its

  > class id to a new PAGE region type (and subtype).

  > (Does not annotate `ReadingOrder` or `TextLine`s or `@orientation`.)

  > Produce a new output file by serialising the resulting hierarchy.

Options:

  -I, --input-file-grp USE        File group(s) used as input

  -O, --output-file-grp USE       File group(s) used as output

  -g, --page-id ID                Physical page ID(s) to process

  --overwrite                     Remove existing output pages/images

                                  (with --page-id, remove only those)

  --profile                       Enable profiling

  --profile-file                  Write cProfile stats to this file. Implies --profile

  -p, --parameter JSON-PATH       Parameters, either verbatim JSON string

                                  or JSON file path

  -P, --param-override KEY VAL    Override a single JSON object key-value pair,

                                  taking precedence over --parameter

  -m, --mets URL-PATH             URL or file path of METS to process

  -w, --working-dir PATH          Working directory of local workspace

  -l, --log-level [OFF|ERROR|WARN|INFO|DEBUG|TRACE]

                                  Log level

  -C, --show-resource RESNAME     Dump the content of processor resource RESNAME

  -L, --list-resources            List names of processor resources

  -J, --dump-json                 Dump tool description as JSON and exit

  -D, --dump-module-dir           Output the 'module' directory with resources for this processor

  -h, --help                      This help message

  -V, --version                   Show version

Parameters:

   "operation_level" [string - "page"]

    hierarchy level which to predict and assign regions for

    Possible values: ["page", "table"]

   "categories" [array - REQUIRED]

    maps each category (class index) of the model to a PAGE region

    type (and @type or @custom if separated by colon), e.g.

    ['TextRegion:paragraph', 'TextRegion:heading',

    'TextRegion:floating', 'TableRegion', 'ImageRegion'] for PubLayNet;

    categories with an empty string will be skipped during prediction

   "model_config" [string - REQUIRED]

    path name of model config

   "model_weights" [string - REQUIRED]

    path name of model weights

   "min_confidence" [number - 0.5]

    confidence threshold for detections

   "postprocessing" [string - "full"]

    which postprocessing steps to enable: by default, applies a custom

    non-maximum suppression (to avoid overlaps) and morphological

    operations (using connected component analysis on the binarized

    input image to shrink or expand regions)

    Possible values: ["full", "only-nms", "only-morph", "none"]

   "debug_img" [string - "none"]

    paint an AlternativeImage which blends the input image

    and all raw decoded region candidates

    Possible values: ["none", "instance_colors", "instance_colors_only", "category_colors"]

   "device" [string - "cuda"]

    select computing device for Torch (e.g. cpu or cuda:0); will fall

    back to CPU if no GPU is available

```

Example:

    # download one preconfigured model:

    ocrd resmgr download ocrd-detectron2-segment TableBank_X152.yaml

    ocrd resmgr download ocrd-detectron2-segment TableBank_X152.pth

    # run it (setting model_config, model_weights and categories):

    ocrd-detectron2-segment -I OCR-D-BIN -O OCR-D-SEG-TAB -P categories '["TableRegion"]' -P model_config TableBank_X152.yaml -P model_weights TableBank_X152.pth -P min_confidence 0.1

    # run it (equivalent, with presets file)

    ocrd-detectron2-segment -I OCR-D-BIN -O OCR-D-SEG-TAB -p presets_TableBank_X152.json -P min_confidence 0.1 

    # download all preconfigured models

    ocrd resmgr download ocrd-detectron2-segment "*"

For installation **via Docker**, usage is bascially the same as above – with some modifications:

    # For data persistency, decide which host-side directories you want to mount in Docker:

    DATADIR=/host-side/path/to/data

    MODELDIR=/host-side/path/to/models

    # Either you "log in" to a container first:

    docker run -v $DATADIR:/data -v $MODELDIR:/usr/local/share/ocrd-resources -it bertsky/ocrd_detectron2 bash

    # and then can use the above commands verbatim

    ...

    # Or you spin up a new container each time,

    # which means prefixing the above commands with

    docker run -v $DATADIR:/data -v $MODELDIR:/usr/local/share/ocrd-resources bertsky/ocrd_detectron2 ...

#### Debugging

If you mistrust your model, and/or this tool's additional postprocessing,

try playing with the runtime parameters:

- Set `debug_img` to some value other than `none`, e.g. `instance_colors_only`.

  This will generate an image which overlays the raw predictions with the raw image

  using Detectron2's internal visualiser. The parameter settings correspond to its

  [ColorMode](https://detectron2.readthedocs.io/en/latest/modules/utils.html#detectron2.utils.visualizer.ColorMode).

  The AlternativeImages will have `@comments="debug"`, and will also be referenced in the METS,

  which allows convenient browsing with [OCR-D Browser](https://github.com/hnesk/browse-ocrd).

  (For example, open the Page View and Image View side by side, and navigate to your output

  fileGrp on each.)

- Selectively disable postprocessing steps: from the default `full` via `only-nms` (first stage)

  or `only-morph` (second stage) to `none`.

- Lower `min_confidence` to get more candidates, raise to get fewer.

## Models

Some of the following models have already been registered as known [file resources](https://ocr-d.de/en/spec/cli#processor-resources), along with parameter presets to use them conveniently.

To get a list of registered models **available for download**, do:

    ocrd resmgr list-available -e ocrd-detectron2-segment

To get a list of **already installed** models and presets, do:

    ocrd resmgr list-installed -e ocrd-detectron2-segment

To **download** a registered model (i.e. a config file and the respective weights file), do:

    ocrd resmgr download ocrd-detectron2-segment NAME.yaml

    ocrd resmgr download ocrd-detectron2-segment NAME.pth

To download more models (registered or other), see:

    ocrd resmgr download --help

To **use** a model, do:

    ocrd-detectron2-segment -P model_config NAME.yaml -P model_weights NAME.pth -P categories '[...]' ...

    ocrd-detectron2-segment -p NAME.json ... # equivalent, with presets file

To add (i.e. register) a **new model**, you first have to find:

- the classes it is trained on, so you can then define a mapping to PAGE-XML region (and subregion) types,

- a download link to the model config and model weights file. 

  Archives (zip/tar) are allowed, but then you must also specify the file paths to extract.

Assuming you have done so, then proceed as follows:

    # from local file path

    ocrd resmgr download -n path/to/model/config.yml ocrd-detectron2-segment NAME.yml

    ocrd resmgr download -n path/to/model/weights.pth ocrd-detectron2-segment NAME.pth

    # from single file URL

    ocrd resmgr download -n https://path.to/model/config.yml ocrd-detectron2-segment NAME.yml

    ocrd resmgr download -n https://path.to/model/weights.pth ocrd-detectron2-segment NAME.pth

    # from zip file URL

    ocrd resmgr download -n https://path.to/model/arch.zip -t archive -P zip-path/to/config.yml ocrd-detectron2-segment NAME.yml

    ocrd resmgr download -n https://path.to/model/arch.zip -t archive -P zip-path/to/weights.pth ocrd-detectron2-segment NAME.pth

    # create corresponding preset file

    echo '{"model_weights": "NAME.pth", "model_config": "NAME.yml", "categories": [...]}' > NAME.json

    # install preset file so it can be used everywhere (not just in CWD):

    ocrd resmgr download -n NAME.json ocrd-detectron2-segment NAME.json

    # now the new model can be used just like the preregistered models

    ocrd-detectron2-segment -p NAME.json ...

What follows is an **overview** of the **preregistered** models (i.e. available via `resmgr`).

> **Note**: These are just examples, no exhaustive search was done yet!

> **Note**: The filename suffix (.pth vs .pkl) of the weight file does matter!

### [TableBank](https://github.com/doc-analysis/TableBank)

X152-FPN [config](https://layoutlm.blob.core.windows.net/tablebank/model_zoo/detection/All_X152/All_X152.yaml)|[weights](https://layoutlm.blob.core.windows.net/tablebank/model_zoo/detection/All_X152/model_final.pth)|`["TableRegion"]`

### [TableBank](https://github.com/Psarpei/Multi-Type-TD-TSR)

X152-FPN [config](https://drive.google.com/drive/folders/1COTV5f7dEAA4Txmxy3LVfcNHiPSc4Bmp?usp=sharing)|[weights](https://drive.google.com/drive/folders/1COTV5f7dEAA4Txmxy3LVfcNHiPSc4Bmp?usp=sharing)|`["TableRegion"]`

### [PubLayNet](https://github.com/hpanwar08/detectron2)

R50-FPN [config](https://github.com/hpanwar08/detectron2/raw/master/configs/DLA_mask_rcnn_R_50_FPN_3x.yaml)|[weights](https://www.dropbox.com/sh/44ez171b2qaocd2/AAB0huidzzOXeo99QdplZRjua)|`["TextRegion:paragraph", "TextRegion:heading", "TextRegion:floating", "TableRegion", "ImageRegion"]`

R101-FPN [config](https://github.com/hpanwar08/detectron2/raw/master/configs/DLA_mask_rcnn_R_101_FPN_3x.yaml)|[weights](https://www.dropbox.com/sh/wgt9skz67usliei/AAD9n6qbsyMz1Y3CwpZpHXCpa)|`["TextRegion:paragraph", "TextRegion:heading", "TextRegion:floating", "TableRegion", "ImageRegion"]`

X101-FPN [config](https://github.com/hpanwar08/detectron2/raw/master/configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml)|[weights](https://www.dropbox.com/sh/1098ym6vhad4zi6/AABe16eSdY_34KGp52W0ruwha)|`["TextRegion:paragraph", "TextRegion:heading", "TextRegion:floating", "TableRegion", "ImageRegion"]`

### [PubLayNet](https://github.com/JPLeoRX/detectron2-publaynet)

R50-FPN [config](https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml)|[weights](https://drive.google.com/file/d/1IbxaRd82hIrxPT4a1U61_g2vvE3zcRLO/view?usp=sharing)|`["TextRegion:paragraph", "TextRegion:heading", "TextRegion:floating", "TableRegion", "ImageRegion"]`

R101-FPN [config](https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml)|[weights](https://drive.google.com/file/d/17MD-FegQtFRNn4GeHqKCLaQZ6FiFrzLg/view?usp=sharing)|`["TextRegion:paragraph", "TextRegion:heading", "TextRegion:floating", "TableRegion", "ImageRegion"]`

### [LayoutParser](https://github.com/Layout-Parser/layout-parser/blob/master/src/layoutparser/models/detectron2/catalog.py)

provides different model variants of various depths for multiple datasets:

- [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) (Medical Research Papers)

- [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) (Tables Computer Typesetting)

- [PRImALayout](https://www.primaresearch.org/dataset/) (Various Computer Typesetting)  

  R50-FPN [config](https://www.dropbox.com/s/yc92x97k50abynt/config.yaml?dl=1)|[weights](https://www.dropbox.com/s/h7th27jfv19rxiy/model_final.pth?dl=1)|`["Background","TextRegion","ImageRegion","TableRegion","MathsRegion","SeparatorRegion","LineDrawingRegion"]`

- [HJDataset](https://dell-research-harvard.github.io/HJDataset/) (Historical Japanese Magazines)

- [NewspaperNavigator](https://news-navigator.labs.loc.gov/) (Historical Newspapers)

- [Math Formula Detection](http://transcriptorium.eu/~htrcontest/MathsICDAR2021/)

See [here](https://github.com/Layout-Parser/layout-parser/blob/master/docs/notes/modelzoo.md) for an overview,

and [here](https://github.com/Layout-Parser/layout-parser/blob/main/src/layoutparser/models/detectron2/catalog.py) for the model files.

You will have to adapt the label map to conform to [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)

region (sub)types accordingly.

### [PubLaynet finetuning](https://github.com/Jambo-sudo/Historical-document-layout-analysis)

(pre-trained on PubLayNet, fine-tuned on a custom, non-public GT corpus of 500 pages 20th century magazines)

X101-FPN [config](https://github.com/Jambo-sudo/Historical-document-layout-analysis/raw/main/historical-document-analysis/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml)|[weights](https://www.dropbox.com/s/hfhsdpvg7jesd4g/pub_model_final.pth?dl=1)|`["TextRegion:caption","ImageRegion","TextRegion:page-number","TableRegion","TextRegion:heading","TextRegion:paragraph"]`

### [DocBank](https://github.com/doc-analysis/DocBank/blob/master/MODEL_ZOO.md)

X101-FPN [archive](https://layoutlm.blob.core.windows.net/docbank/model_zoo/X101.zip)

Proposed mappings:

- `["TextRegion:header", "TextRegion:credit", "TextRegion:caption", "TextRegion:other", "MathsRegion", "GraphicRegion", "TextRegion:footer", "TextRegion:floating", "TextRegion:paragraph", "TextRegion:endnote", "TextRegion:heading", "TableRegion", "TextRegion:heading"]` (using only predefined `@type`)

- `["TextRegion:abstract", "TextRegion:author", "TextRegion:caption", "TextRegion:date", "MathsRegion", "GraphicRegion", "TextRegion:footer", "TextRegion:list", "TextRegion:paragraph", "TextRegion:reference", "TextRegion:heading", "TableRegion", "TextRegion:title"]` (using `@custom` as well)

## Testing

To install Python dependencies and download some models:

    make deps-test

Which is the equivalent of:

    pip install -r requirements-test.txt

    make models-test

To run the tests, then do:

    make test

You can inspect the results under `test/assets/*/data` under various new `OCR-D-SEG-*` fileGrps.

(Again, it is recommended to use [OCR-D Browser](https://github.com/hnesk/browse-ocrd).)

Finally, to remove the test data, do:

    make clean

### Test results

These tests are integrated as a [Github Action](https://github.com/bertsky/ocrd_detectron2/actions/workflows/python-app.yml). Its results can be viewed [here](https://bertsky.github.io/ocrd_detectron2/test-results).