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unified_detector

README.md

Towards End-to-End Unified Scene Text Detection and Layout Analysis

unified detection

UnifiedDetector

Official TensorFlow 2 implementation of the paper Towards End-to-End Unified Scene Text Detection and Layout Analysis. If you encounter any issues using the code, you are welcome to submit them to the Issues tab or send emails directly to us: hiertext@google.com.

Installation

Set up TensorFlow Models

# (Optional) Create and enter a virtual environment
pip3 install --user virtualenv
virtualenv -p python3 unified_detector
source ./unified_detector/bin/activate

# First clone the TensorFlow Models project:
git clone https://github.com/tensorflow/models.git

# Install the requirements of TensorFlow Models and this repo:
cd models
pip3 install -r official/requirements.txt
pip3 install -r official/projects/unified_detector/requirements.txt

# Compile the protos
# If `protoc` is not installed, please follow: https://grpc.io/docs/protoc-installation/
export PYTHONPATH=${PYTHONPATH}:${PWD}/research/
cd research/object_detection/
protoc protos/string_int_label_map.proto --python_out=.

Set up Deeplab2

# Clone Deeplab2 anywhere you like
cd <somewhere>
git clone https://github.com/google-research/deeplab2.git

# Compile the protos
protoc deeplab2/*.proto --python_out=.

# Add to PYTHONPATH the directory where deeplab2 sits.
export PYTHONPATH=${PYTHONPATH}:${PWD}

Running the model on some images using the provided checkpoint.

Download the checkpoint

Model Input Resolution #object query line PQ (val) paragraph PQ (val) line PQ (test) paragraph PQ (test)
Unified-Detector-Line (ckpt) 1024 384 61.04 52.84 62.20 53.52

Demo on single images

# run from `models/`
python3 -m official.projects.unified_detector.run_inference \
--gin_file=official/projects/unified_detector/configs/gin_files/unified_detector_model.gin \
--ckpt_path=<path-of-the-ckpt> \
--img_file=<some-image> \
--output_path=<some-directory>/demo.jsonl \
--vis_dir=<some-directory>

The output will be stored in jsonl in the same hierarchical format as required by the evaluation script of the HierText dataset. There will also be visualizations of the word/line/paragraph boundaries. Note that, the unified detector produces line-level masks and an affinity matrix for grouping lines into paragraphs. For visualization purpose, we split each line mask into pixel groups which are defined as connected components/pixels. We visualize these groups as words. They are not necessarily at the word granularity, though. We visualize lines and paragraphs as groupings of these words using axis-aligned bounding boxes.

FAQ
  1. Q: What is ckpt_path? A: If you download the checkpoint as instructed above, you will obtain two files: <some-directory>/ckpt.index and <some-directory>/ckpt.data-00000-of-00001. You need to set --ckpt_path=<some-directory>/ckpt, i.e. removing the suffix.

Inference and Evaluation on the HierText dataset

Download the HierText dataset

Clone the HierText repo and download the dataset. The requirements.txt in this folder already covers those in the HierText repo, so there is no need to create a new virtual environment again.

Inference and eval

The following command will run the model on the validation set and compute the score. Note that the test set annotation is not released yet, so only validation set is used here for demo purposes.

Inference

# Run from `models/`
python3 -m official.projects.unified_detector.run_inference \
--gin_file=official/projects/unified_detector/configs/gin_files/unified_detector_model.gin \
--ckpt_path=<path-of-the-ckpt> \
--img_dir=<the-directory-containing-validation-images> \
--output_path=<some-directory>/validation_output.jsonl

Evaluation

# Run from `hiertext/`
python3 eval.py \
--gt=gt/validation.jsonl \
--result=<some-directory>/validation_output.jsonl \
--output=./validation-score.txt \
--mask_stride=1 \
--eval_lines \
--eval_paragraphs \
--num_workers=0

Train new models.

First, you will need to convert the HierText dataset into TFrecords:

# Run from `models/official/projects/unified_detector/data_conversion`
CUDA_VISIBLE_DEVICES='' python3 convert.py \
--gt_file=/path/to/gt.jsonl \
--img_dir=/path/to/image \
--out_file=/path/to/tfrecords/file-prefix

To train the unified detector, run the following script:

# Run from `models/`
python3 -m official.projects.unified_detector.train \
--mode=train \
--experiment=unified_detector \
--model_dir='<some path>' \
--gin_file='official/projects/unified_detector/configs/gin_files/unified_detector_train.gin' \
--gin_file='official/projects/unified_detector/configs/gin_files/unified_detector_model.gin' \
--gin_params='InputFn.input_paths = ["/path/to/tfrecords/file-prefix*"]'

Citation

Please cite our paper if you find this work helpful:

@inproceedings{long2022towards,
  title={Towards End-to-End Unified Scene Text Detection and Layout Analysis},
  author={Long, Shangbang and Qin, Siyang and Panteleev, Dmitry and Bissacco, Alessandro and Fujii, Yasuhisa and Raptis, Michalis},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}