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""" |
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Paper: "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents" presented at ICDAR 2023 |
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Authors: Abdur Rahman, Arjun Ghosh, Chetan Arora |
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GitHub Repository: https://github.com/abdur75648/UTRNet-High-Resolution-Urdu-Text-Recognition |
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Project Website: https://abdur75648.github.io/UTRNet/ |
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Copyright (c) 2023-present: This work is licensed under the Creative Commons Attribution-NonCommercial |
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4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) |
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""" |
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import torch.nn as nn |
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from .densenet import DenseNet |
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from .hrnet import HRNet |
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from .inception_unet import InceptionUNet |
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from .rcnn import RCNN |
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from .resnet import ResNet |
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from .resunet import ResUnet |
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from .unet_attn import AttnUNet |
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from .unet_plus_plus import NestedUNet |
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from .unet import UNet |
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from .vgg import VGG |
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class DenseNet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(DenseNet_FeatureExtractor, self).__init__() |
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self.ConvNet = DenseNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class HRNet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=32): |
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super(HRNet_FeatureExtractor, self).__init__() |
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self.ConvNet = HRNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class InceptionUNet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(InceptionUNet_FeatureExtractor, self).__init__() |
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self.ConvNet = InceptionUNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class RCNN_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(RCNN_FeatureExtractor, self).__init__() |
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self.ConvNet = RCNN(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class ResNet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(ResNet_FeatureExtractor, self).__init__() |
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self.ConvNet = ResNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class ResUnet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(ResUnet_FeatureExtractor, self).__init__() |
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self.ConvNet = ResUnet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class AttnUNet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(AttnUNet_FeatureExtractor, self).__init__() |
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self.ConvNet = AttnUNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class UNet_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(UNet_FeatureExtractor, self).__init__() |
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self.ConvNet = UNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class UNetPlusPlus_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(UNetPlusPlus_FeatureExtractor, self).__init__() |
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self.ConvNet = NestedUNet(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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class VGG_FeatureExtractor(nn.Module): |
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def __init__(self, input_channel=1, output_channel=512): |
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super(VGG_FeatureExtractor, self).__init__() |
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self.ConvNet = VGG(input_channel, output_channel) |
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def forward(self, input): |
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return self.ConvNet(input) |
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