doctr.models - docTR documentation (original) (raw)

doctr.models.classification¶

doctr.models.classification.vgg16_bn_r(pretrained: bool = False, **kwargs: Any) → VGG[source]¶

VGG-16 architecture as described in “Very Deep Convolutional Networks for Large-Scale Image Recognition”, modified by adding batch normalization, rectangular pooling and a simpler classification head.

import torch from doctr.models import vgg16_bn_r model = vgg16_bn_r(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on ImageNet
**kwargs – keyword arguments of the VGG architecture

Returns:

VGG feature extractor

doctr.models.classification.resnet18(pretrained: bool = False, **kwargs: Any) → ResNet[source]¶

ResNet-18 architecture as described in “Deep Residual Learning for Image Recognition”,.

import torch from doctr.models import resnet18 model = resnet18(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the ResNet architecture

Returns:

A resnet18 model

doctr.models.classification.resnet34(pretrained: bool = False, **kwargs: Any) → ResNet[source]¶

ResNet-34 architecture as described in “Deep Residual Learning for Image Recognition”,.

import torch from doctr.models import resnet34 model = resnet34(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the ResNet architecture

Returns:

A resnet34 model

doctr.models.classification.resnet50(pretrained: bool = False, **kwargs: Any) → ResNet[source]¶

ResNet-50 architecture as described in “Deep Residual Learning for Image Recognition”,.

import torch from doctr.models import resnet50 model = resnet50(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the ResNet architecture

Returns:

A resnet50 model

doctr.models.classification.resnet31(pretrained: bool = False, **kwargs: Any) → ResNet[source]¶

Resnet31 architecture with rectangular pooling windows as described in“Show, Attend and Read:A Simple and Strong Baseline for Irregular Text Recognition”,. Downsizing: (H, W) –> (H/8, W/4)

import torch from doctr.models import resnet31 model = resnet31(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the ResNet architecture

Returns:

A resnet31 model

doctr.models.classification.mobilenet_v3_small(pretrained: bool = False, **kwargs: Any) → MobileNetV3[source]¶

MobileNetV3-Small architecture as described in“Searching for MobileNetV3”,.

import torch from doctr.models import mobilenet_v3_small model = mobilenetv3_small(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the MobileNetV3 architecture

Returns:

a torch.nn.Module

doctr.models.classification.mobilenet_v3_large(pretrained: bool = False, **kwargs: Any) → MobileNetV3[source]¶

MobileNetV3-Large architecture as described in“Searching for MobileNetV3”,.

import torch from doctr.models import mobilenet_v3_large model = mobilenet_v3_large(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the MobileNetV3 architecture

Returns:

a torch.nn.Module

doctr.models.classification.mobilenet_v3_small_r(pretrained: bool = False, **kwargs: Any) → MobileNetV3[source]¶

MobileNetV3-Small architecture as described in“Searching for MobileNetV3”,, with rectangular pooling.

import torch from doctr.models import mobilenet_v3_small_r model = mobilenet_v3_small_r(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the MobileNetV3 architecture

Returns:

a torch.nn.Module

doctr.models.classification.mobilenet_v3_large_r(pretrained: bool = False, **kwargs: Any) → MobileNetV3[source]¶

MobileNetV3-Large architecture as described in“Searching for MobileNetV3”,, with rectangular pooling.

import torch from doctr.models import mobilenet_v3_large_r model = mobilenet_v3_large_r(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the MobileNetV3 architecture

Returns:

a torch.nn.Module

doctr.models.classification.mobilenet_v3_small_crop_orientation(pretrained: bool = False, **kwargs: Any) → MobileNetV3[source]¶

MobileNetV3-Small architecture as described in“Searching for MobileNetV3”,.

import torch from doctr.models import mobilenet_v3_small_crop_orientation model = mobilenet_v3_small_crop_orientation(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the MobileNetV3 architecture

Returns:

a torch.nn.Module

doctr.models.classification.mobilenet_v3_small_page_orientation(pretrained: bool = False, **kwargs: Any) → MobileNetV3[source]¶

MobileNetV3-Small architecture as described in“Searching for MobileNetV3”,.

import torch from doctr.models import mobilenet_v3_small_page_orientation model = mobilenet_v3_small_page_orientation(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the MobileNetV3 architecture

Returns:

a torch.nn.Module

doctr.models.classification.magc_resnet31(pretrained: bool = False, **kwargs: Any) → ResNet[source]¶

Resnet31 architecture with Multi-Aspect Global Context Attention as described in“MASTER: Multi-Aspect Non-local Network for Scene Text Recognition”,.

import torch from doctr.models import magc_resnet31 model = magc_resnet31(pretrained=False) input_tensor = torch.rand((1, 3, 224, 224), dtype=tf.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the ResNet architecture

Returns:

A feature extractor model

doctr.models.classification.vit_s(pretrained: bool = False, **kwargs: Any) → VisionTransformer[source]¶

VisionTransformer-S architecture“An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”,. Patches: (H, W) -> (H/8, W/8)

NOTE: unofficial config used in ViTSTR and ParSeq

import torch from doctr.models import vit_s model = vit_s(pretrained=False) input_tensor = torch.rand((1, 3, 32, 32), dtype=tf.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the VisionTransformer architecture

Returns:

A feature extractor model

doctr.models.classification.vit_b(pretrained: bool = False, **kwargs: Any) → VisionTransformer[source]¶

VisionTransformer-B architecture as described in“An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”,. Patches: (H, W) -> (H/8, W/8)

import torch from doctr.models import vit_b model = vit_b(pretrained=False) input_tensor = torch.rand((1, 3, 32, 32), dtype=tf.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the VisionTransformer architecture

Returns:

A feature extractor model

doctr.models.classification.textnet_tiny(pretrained: bool = False, **kwargs: Any) → TextNet[source]¶

Implements TextNet architecture from “FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation”. Implementation based on the official Pytorch implementation: <https://github.com/czczup/FAST>`_.

import torch from doctr.models import textnet_tiny model = textnet_tiny(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the TextNet architecture

Returns:

A textnet tiny model

doctr.models.classification.textnet_small(pretrained: bool = False, **kwargs: Any) → TextNet[source]¶

import torch from doctr.models import textnet_small model = textnet_small(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the TextNet architecture

Returns:

A TextNet small model

doctr.models.classification.textnet_base(pretrained: bool = False, **kwargs: Any) → TextNet[source]¶

import torch from doctr.models import textnet_base model = textnet_base(pretrained=False) input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained – boolean, True if model is pretrained
**kwargs – keyword arguments of the TextNet architecture

Returns:

A TextNet base model

doctr.models.classification.vip_tiny(pretrained: bool = False, **kwargs: Any) → VIPNet[source]¶

VIP-Tiny encoder architecture.Corresponds to SVIPTRv2-T variant in the paper (VIPTRv2 function in the official implementation:https://github.com/cxfyxl/VIPTR/blob/main/modules/VIPTRv2.py)

Parameters:

pretrained – whether to load pretrained weights
**kwargs – optional arguments

Returns:

VIPNet model

doctr.models.classification.vip_base(pretrained: bool = False, **kwargs: Any) → VIPNet[source]¶

VIP-Base encoder architecture. Corresponds to SVIPTRv2-B variant in the paper (VIPTRv2B function in the official implementation:https://github.com/cxfyxl/VIPTR/blob/main/modules/VIPTRv2.py)

Parameters:

pretrained – whether to load pretrained weights
**kwargs – optional arguments

Returns:

VIPNet model

doctr.models.classification.crop_orientation_predictor(arch: Any = 'mobilenet_v3_small_crop_orientation', pretrained: bool = False, batch_size: int = 128, **kwargs: Any) → OrientationPredictor[source]¶

Crop orientation classification architecture.

import numpy as np from doctr.models import crop_orientation_predictor model = crop_orientation_predictor(arch='mobilenet_v3_small_crop_orientation', pretrained=True) input_crop = (255 * np.random.rand(256, 256, 3)).astype(np.uint8) out = model([input_crop])

Parameters:

arch – name of the architecture to use (e.g. ‘mobilenet_v3_small_crop_orientation’)
pretrained – If True, returns a model pre-trained on our recognition crops dataset
batch_size – number of samples the model processes in parallel
**kwargs – keyword arguments to be passed to the OrientationPredictor

Returns:

OrientationPredictor

doctr.models.classification.page_orientation_predictor(arch: Any = 'mobilenet_v3_small_page_orientation', pretrained: bool = False, batch_size: int = 4, **kwargs: Any) → OrientationPredictor[source]¶

Page orientation classification architecture.

import numpy as np from doctr.models import page_orientation_predictor model = page_orientation_predictor(arch='mobilenet_v3_small_page_orientation', pretrained=True) input_page = (255 * np.random.rand(512, 512, 3)).astype(np.uint8) out = model([input_page])

Parameters:

arch – name of the architecture to use (e.g. ‘mobilenet_v3_small_page_orientation’)
pretrained – If True, returns a model pre-trained on our recognition crops dataset
batch_size – number of samples the model processes in parallel
**kwargs – keyword arguments to be passed to the OrientationPredictor

Returns:

OrientationPredictor

doctr.models.detection¶

doctr.models.detection.linknet_resnet18(pretrained: bool = False, **kwargs: Any) → LinkNet[source]¶

LinkNet as described in “LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation”.

import torch from doctr.models import linknet_resnet18 model = linknet_resnet18(pretrained=True).eval() input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the LinkNet architecture

Returns:

text detection architecture

doctr.models.detection.linknet_resnet34(pretrained: bool = False, **kwargs: Any) → LinkNet[source]¶

LinkNet as described in “LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation”.

import torch from doctr.models import linknet_resnet34 model = linknet_resnet34(pretrained=True).eval() input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the LinkNet architecture

Returns:

text detection architecture

doctr.models.detection.linknet_resnet50(pretrained: bool = False, **kwargs: Any) → LinkNet[source]¶

LinkNet as described in “LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation”.

import torch from doctr.models import linknet_resnet50 model = linknet_resnet50(pretrained=True).eval() input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the LinkNet architecture

Returns:

text detection architecture

doctr.models.detection.db_resnet50(pretrained: bool = False, **kwargs: Any) → DBNet[source]¶

DBNet as described in “Real-time Scene Text Detection with Differentiable Binarization”, using a ResNet-50 backbone.

import torch from doctr.models import db_resnet50 model = db_resnet50(pretrained=True) input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the DBNet architecture

Returns:

text detection architecture

doctr.models.detection.db_mobilenet_v3_large(pretrained: bool = False, **kwargs: Any) → DBNet[source]¶

DBNet as described in “Real-time Scene Text Detection with Differentiable Binarization”, using a MobileNet V3 Large backbone.

import torch from doctr.models import db_mobilenet_v3_large model = db_mobilenet_v3_large(pretrained=True) input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the DBNet architecture

Returns:

text detection architecture

doctr.models.detection.fast_tiny(pretrained: bool = False, **kwargs: Any) → FAST[source]¶

FAST as described in “FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation”, using a tiny TextNet backbone.

import torch from doctr.models import fast_tiny model = fast_tiny(pretrained=True) input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the DBNet architecture

Returns:

text detection architecture

doctr.models.detection.fast_small(pretrained: bool = False, **kwargs: Any) → FAST[source]¶

FAST as described in “FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation”, using a small TextNet backbone.

import torch from doctr.models import fast_small model = fast_small(pretrained=True) input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the DBNet architecture

Returns:

text detection architecture

doctr.models.detection.fast_base(pretrained: bool = False, **kwargs: Any) → FAST[source]¶

FAST as described in “FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation”, using a base TextNet backbone.

import torch from doctr.models import fast_base model = fast_base(pretrained=True) input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text detection dataset
**kwargs – keyword arguments of the DBNet architecture

Returns:

text detection architecture

doctr.models.detection.detection_predictor(arch: Any = 'fast_base', pretrained: bool = False, assume_straight_pages: bool = True, preserve_aspect_ratio: bool = True, symmetric_pad: bool = True, batch_size: int = 2, **kwargs: Any) → DetectionPredictor[source]¶

Text detection architecture.

import numpy as np from doctr.models import detection_predictor model = detection_predictor(arch='db_resnet50', pretrained=True) input_page = (255 * np.random.rand(600, 800, 3)).astype(np.uint8) out = model([input_page])

Parameters:

arch – name of the architecture or model itself to use (e.g. ‘db_resnet50’)
pretrained – If True, returns a model pre-trained on our text detection dataset
assume_straight_pages – If True, fit straight boxes to the page
preserve_aspect_ratio – If True, pad the input document image to preserve the aspect ratio before running the detection model on it
symmetric_pad – if True, pad the image symmetrically instead of padding at the bottom-right
batch_size – number of samples the model processes in parallel
**kwargs – optional keyword arguments passed to the architecture

Returns:

Detection predictor

doctr.models.recognition¶

doctr.models.recognition.crnn_vgg16_bn(pretrained: bool = False, **kwargs: Any) → CRNN[source]¶

CRNN with a VGG-16 backbone as described in “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition”.

import torch from doctr.models import crnn_vgg16_bn model = crnn_vgg16_bn(pretrained=True) input_tensor = torch.rand(1, 3, 32, 128) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keyword arguments of the CRNN architecture

Returns:

text recognition architecture

doctr.models.recognition.crnn_mobilenet_v3_small(pretrained: bool = False, **kwargs: Any) → CRNN[source]¶

CRNN with a MobileNet V3 Small backbone as described in “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition”.

import torch from doctr.models import crnn_mobilenet_v3_small model = crnn_mobilenet_v3_small(pretrained=True) input_tensor = torch.rand(1, 3, 32, 128) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keyword arguments of the CRNN architecture

Returns:

text recognition architecture

doctr.models.recognition.crnn_mobilenet_v3_large(pretrained: bool = False, **kwargs: Any) → CRNN[source]¶

CRNN with a MobileNet V3 Large backbone as described in “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition”.

import torch from doctr.models import crnn_mobilenet_v3_large model = crnn_mobilenet_v3_large(pretrained=True) input_tensor = torch.rand(1, 3, 32, 128) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keyword arguments of the CRNN architecture

Returns:

text recognition architecture

doctr.models.recognition.sar_resnet31(pretrained: bool = False, **kwargs: Any) → SAR[source]¶

SAR with a resnet-31 feature extractor as described in “Show, Attend and Read:A Simple and Strong Baseline for Irregular Text Recognition”.

import torch from doctr.models import sar_resnet31 model = sar_resnet31(pretrained=False) input_tensor = torch.rand((1, 3, 32, 128)) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keyword arguments of the SAR architecture

Returns:

text recognition architecture

doctr.models.recognition.master(pretrained: bool = False, **kwargs: Any) → MASTER[source]¶

MASTER as described in paper: <https://arxiv.org/pdf/1910.02562.pdf>`_.

import torch from doctr.models import master model = master(pretrained=False) input_tensor = torch.rand((1, 3, 32, 128)) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keywoard arguments passed to the MASTER architecture

Returns:

text recognition architecture

doctr.models.recognition.vitstr_small(pretrained: bool = False, **kwargs: Any) → ViTSTR[source]¶

ViTSTR-Small as described in “Vision Transformer for Fast and Efficient Scene Text Recognition”.

import torch from doctr.models import vitstr_small model = vitstr_small(pretrained=False) input_tensor = torch.rand((1, 3, 32, 128)) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
kwargs – keyword arguments of the ViTSTR architecture

Returns:

text recognition architecture

doctr.models.recognition.vitstr_base(pretrained: bool = False, **kwargs: Any) → ViTSTR[source]¶

ViTSTR-Base as described in “Vision Transformer for Fast and Efficient Scene Text Recognition”.

import torch from doctr.models import vitstr_base model = vitstr_base(pretrained=False) input_tensor = torch.rand((1, 3, 32, 128)) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
kwargs – keyword arguments of the ViTSTR architecture

Returns:

text recognition architecture

doctr.models.recognition.parseq(pretrained: bool = False, **kwargs: Any) → PARSeq[source]¶

PARSeq architecture from“Scene Text Recognition with Permuted Autoregressive Sequence Models”.

import torch from doctr.models import parseq model = parseq(pretrained=False) input_tensor = torch.rand((1, 3, 32, 128)) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keyword arguments of the PARSeq architecture

Returns:

text recognition architecture

doctr.models.recognition.viptr_tiny(pretrained: bool = False, **kwargs: Any) → VIPTR[source]¶

VIPTR-Tiny as described in “A Vision Permutable Extractor for Fast and Efficient Scene Text Recognition”.

import torch from doctr.models import viptr_tiny model = viptr_tiny(pretrained=False) input_tensor = torch.rand((1, 3, 32, 128)) out = model(input_tensor)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on our text recognition dataset
**kwargs – keyword arguments of the VIPTR architecture

Returns:

a VIPTR model instance

Return type:

VIPTR

doctr.models.recognition.recognition_predictor(arch: Any = 'crnn_vgg16_bn', pretrained: bool = False, symmetric_pad: bool = False, batch_size: int = 128, **kwargs: Any) → RecognitionPredictor[source]¶

Text recognition architecture.

Example::

import numpy as np from doctr.models import recognition_predictor model = recognition_predictor(pretrained=True) input_page = (255 * np.random.rand(32, 128, 3)).astype(np.uint8) out = model([input_page])

Parameters:

arch – name of the architecture or model itself to use (e.g. ‘crnn_vgg16_bn’)
pretrained – If True, returns a model pre-trained on our text recognition dataset
symmetric_pad – if True, pad the image symmetrically instead of padding at the bottom-right
batch_size – number of samples the model processes in parallel
**kwargs – optional parameters to be passed to the architecture

Returns:

Recognition predictor

doctr.models.zoo¶

doctr.models.ocr_predictor(det_arch: Any = 'fast_base', reco_arch: Any = 'crnn_vgg16_bn', pretrained: bool = False, pretrained_backbone: bool = True, assume_straight_pages: bool = True, preserve_aspect_ratio: bool = True, symmetric_pad: bool = True, export_as_straight_boxes: bool = False, detect_orientation: bool = False, straighten_pages: bool = False, detect_language: bool = False, **kwargs: Any) → OCRPredictor[source]¶

End-to-end OCR architecture using one model for localization, and another for text recognition.

import numpy as np from doctr.models import ocr_predictor model = ocr_predictor('db_resnet50', 'crnn_vgg16_bn', pretrained=True) input_page = (255 * np.random.rand(600, 800, 3)).astype(np.uint8) out = model([input_page])

Parameters:

det_arch – name of the detection architecture or the model itself to use (e.g. ‘db_resnet50’, ‘db_mobilenet_v3_large’)
reco_arch – name of the recognition architecture or the model itself to use (e.g. ‘crnn_vgg16_bn’, ‘sar_resnet31’)
pretrained – If True, returns a model pre-trained on our OCR dataset
pretrained_backbone – If True, returns a model with a pretrained backbone
assume_straight_pages – if True, speeds up the inference by assuming you only pass straight pages without rotated textual elements.
preserve_aspect_ratio – If True, pad the input document image to preserve the aspect ratio before running the detection model on it.
symmetric_pad – if True, pad the image symmetrically instead of padding at the bottom-right.
export_as_straight_boxes – when assume_straight_pages is set to False, export final predictions (potentially rotated) as straight bounding boxes.
detect_orientation – if True, the estimated general page orientation will be added to the predictions for each page. Doing so will slightly deteriorate the overall latency.
straighten_pages – if True, estimates the page general orientation based on the segmentation map median line orientation. Then, rotates page before passing it again to the deep learning detection module. Doing so will improve performances for documents with page-uniform rotations.
detect_language – if True, the language prediction will be added to the predictions for each page. Doing so will slightly deteriorate the overall latency.
kwargs – keyword args of OCRPredictor

Returns:

OCR predictor

doctr.models.kie_predictor(det_arch: Any = 'fast_base', reco_arch: Any = 'crnn_vgg16_bn', pretrained: bool = False, pretrained_backbone: bool = True, assume_straight_pages: bool = True, preserve_aspect_ratio: bool = True, symmetric_pad: bool = True, export_as_straight_boxes: bool = False, detect_orientation: bool = False, straighten_pages: bool = False, detect_language: bool = False, **kwargs: Any) → KIEPredictor[source]¶

End-to-end KIE architecture using one model for localization, and another for text recognition.

import numpy as np from doctr.models import ocr_predictor model = ocr_predictor('db_resnet50', 'crnn_vgg16_bn', pretrained=True) input_page = (255 * np.random.rand(600, 800, 3)).astype(np.uint8) out = model([input_page])

Parameters:

det_arch – name of the detection architecture or the model itself to use (e.g. ‘db_resnet50’, ‘db_mobilenet_v3_large’)
reco_arch – name of the recognition architecture or the model itself to use (e.g. ‘crnn_vgg16_bn’, ‘sar_resnet31’)
pretrained – If True, returns a model pre-trained on our OCR dataset
pretrained_backbone – If True, returns a model with a pretrained backbone
assume_straight_pages – if True, speeds up the inference by assuming you only pass straight pages without rotated textual elements.
preserve_aspect_ratio – If True, pad the input document image to preserve the aspect ratio before running the detection model on it.
symmetric_pad – if True, pad the image symmetrically instead of padding at the bottom-right.
export_as_straight_boxes – when assume_straight_pages is set to False, export final predictions (potentially rotated) as straight bounding boxes.
detect_orientation – if True, the estimated general page orientation will be added to the predictions for each page. Doing so will slightly deteriorate the overall latency.
straighten_pages – if True, estimates the page general orientation based on the segmentation map median line orientation. Then, rotates page before passing it again to the deep learning detection module. Doing so will improve performances for documents with page-uniform rotations.
detect_language – if True, the language prediction will be added to the predictions for each page. Doing so will slightly deteriorate the overall latency.
kwargs – keyword args of OCRPredictor

Returns:

KIE predictor

doctr.models.factory¶

doctr.models.factory.login_to_hub() → None [source]¶

doctr.models.factory.from_hub(repo_id: str, **kwargs: Any)[source]¶

Instantiate & load a pretrained model from HF hub.

from doctr.models import from_hub model = from_hub("mindee/fasterrcnn_mobilenet_v3_large_fpn")

Parameters:

repo_id – HuggingFace model hub repo
kwargs – kwargs of hf_hub_download or snapshot_download

Returns:

Model loaded with the checkpoint

doctr.models.factory.push_to_hf_hub(model: Any, model_name: str, task: str, **kwargs) → None [source]¶

Save model and its configuration on HF hub

from doctr.models import login_to_hub, push_to_hf_hub from doctr.models.recognition import crnn_mobilenet_v3_small login_to_hub() model = crnn_mobilenet_v3_small(pretrained=True) push_to_hf_hub(model, 'my-model', 'recognition', arch='crnn_mobilenet_v3_small')

Parameters:

model – PyTorch model to be saved
model_name – name of the model which is also the repository name
task – task name
**kwargs – keyword arguments for push_to_hf_hub