Reading Pipeline

This is a pipeline for text detection and reading. It combines the OCR and Segmentation models into the single pipeline and allows to segment an input image, then crop text regions from it and, finally, read these texts using OCR.

Demo

A web demo (on hugging face) of ReadingPipeline for the Peter the Great dataset and web demo for recognition school notebook dataset.

Also there is a demo with an example of using the ReadingPipeline (you can run it in your Google Colab).

Models

Weights for reading manuscripts of Peter the Great, and Peter dataset

Weights for reading school notebooks handwritten dataset, and school notebook datasets itself: RU data and EN data

Quick setup and start

Nvidia drivers >= 470, CUDA >= 11.4
Docker, nvidia-docker

The provided Dockerfile is supplied to build an image with CUDA support and cuDNN.

Preparations

Clone the repo.
Download weights and config-files of segmentation and OCR models to the data/ folder.
sudo make all to build a docker image and create a container. Or sudo make all GPUS=device=0 CPUS=10 if you want to specify gpu devices and limit CPU-resources.

If you don't want to use Docker, you can install dependencies via requirements.txt

Configuring the pipeline

You can change parameters of the pipeline in the pipeline_config.json.

Main pipeline loop

The main_process-dict defines the order of the main processing methods that make up the pipeline loop. Classes are initialized with the parameters specified in the config, and are called one after the other in the predefined order.

PipelinePredictor - the class responsible for assembling the pipeline, and is located in ocrpipeline/predictor.py. To add a new class to the pipeline, you need to add it to the MAIN_PROCESS_DICT dictionary in ocrpipeline/predictor.py and also specify it in the main_process-dict in the config at the point in the chain from which the class should be called.

"main_process": {
    "SegmPrediction": {...},
    "RestoreImageAngle": {...},
    "ClassContourPosptrocess": {...},
    "OCRPrediction": {...},
    "LineFinder": {...},
    ...
}

Models runtime, ONNX

You can specify runtime method for OCR and segmentation models.

"main_process": {
    "SegmPrediction": {
        "model_path": "/path/to/model.ckpt",
        "config_path": "/path/to/config.json",
        "num_threads": 8,
        "device": "cuda",
        "runtime": "Pytorch"  # here you can chose runtime method
    },
    ...
}

You can chose runtime method from several options: "Pytorch" (cuda and cpu devices), "ONNX" (only cpu is allowed) or "OpenVino" (only cpu).

Class specific parameters

Parameters in the classes-dict are set individually for each class. The names of the classes must correspond to the class names of the segmentation model.

The contour_posprocess-dict defines the order of the contour processing, predicted by the segmentation model. Classes are initialized with the parameters specified in the config, and are called one after the other in the predefined order.

ClassContourPosptrocess is the class responsible for assembling and calling contour_posptrocess methods, and is located in ocrpipeline/predictor.py. To add a new class to the pipeline, you need to add it to the CONTOUR_PROCESS_DICT dictionary in ocrpipeline/predictor.py and also specify it in the contour_posprocess-dict in the config at the point in the chain from which the class should be called.

"classes": {
    "shrinked_pupil_text": {
        "contour_posptrocess": {
            "BboxFromContour": {},
            "UpscaleBbox": {"upscale_bbox": [1.4, 2.3]}
        }
    },
	...
}

Inference

An example of model inference can be found in inference_pipeline.ipynb.

To evaluate the pipeline accuracy (the OCR-model combined with the SEGM-model), you can use evaluate script (you first need to generate model predictions, an example in inference_pipeline_on_dataset.ipynb).

lifeformotion/ReadingPipeline