TrOCR
Last updated
Last updated
The TrOCR model was proposed in by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to perform .
The abstract from the paper is the following:
Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition tasks.
Please refer to the VisionEncoderDecoder class on how to use this model.
Tips:
A list of official BOINC AI and community (indicated by 🌎) resources to help you get started with TrOCR. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Text Classification
Text Generation
⚡️ Inference
Step-by-step Optical Character Recognition (OCR)
Copied
( vocab_size = 50265d_model = 1024decoder_layers = 12decoder_attention_heads = 16decoder_ffn_dim = 4096activation_function = 'gelu'max_position_embeddings = 512dropout = 0.1attention_dropout = 0.0activation_dropout = 0.0decoder_start_token_id = 2init_std = 0.02decoder_layerdrop = 0.0use_cache = Truescale_embedding = Falseuse_learned_position_embeddings = Truelayernorm_embedding = Truepad_token_id = 1bos_token_id = 0eos_token_id = 2**kwargs )
Parameters
d_model (int, optional, defaults to 1024) — Dimensionality of the layers and the pooler layer.
decoder_layers (int, optional, defaults to 12) — Number of decoder layers.
decoder_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (int, optional, defaults to 4096) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
activation_function (str or function, optional, defaults to "gelu") — The non-linear activation function (function or string) in the pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported.
max_position_embeddings (int, optional, defaults to 512) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
dropout (float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, and pooler.
attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
activation_dropout (float, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
init_std (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models).
scale_embedding (bool, optional, defaults to False) — Whether or not to scale the word embeddings by sqrt(d_model).
use_learned_position_embeddings (bool, optional, defaults to True) — Whether or not to use learned position embeddings. If not, sinusoidal position embeddings will be used.
layernorm_embedding (bool, optional, defaults to True) — Whether or not to use a layernorm after the word + position embeddings.
Example:
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( image_processor = Nonetokenizer = None**kwargs )
Parameters
image_processor ([ViTImageProcessor/DeiTImageProcessor]) — An instance of [ViTImageProcessor/DeiTImageProcessor]. The image processor is a required input.
tokenizer ([RobertaTokenizer/XLMRobertaTokenizer]) — An instance of [RobertaTokenizer/XLMRobertaTokenizer]. The tokenizer is a required input.
Constructs a TrOCR processor which wraps a vision image processor and a TrOCR tokenizer into a single processor.
__call__
( *args**kwargs )
When used in normal mode, this method forwards all its arguments to AutoImageProcessor’s __call__() and returns its output. If used in the context as_target_processor() this method forwards all its arguments to TrOCRTokenizer’s ~TrOCRTokenizer.__call__. Please refer to the doctsring of the above two methods for more information.
from_pretrained
( pretrained_model_name_or_path: typing.Union[str, os.PathLike]cache_dir: typing.Union[str, os.PathLike, NoneType] = Noneforce_download: bool = Falselocal_files_only: bool = Falsetoken: typing.Union[bool, str, NoneType] = Nonerevision: str = 'main'**kwargs )
Parameters
pretrained_model_name_or_path (str or os.PathLike) — This can be either:
a string, the model id of a pretrained feature_extractor hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
Instantiate a processor associated with a pretrained model.
save_pretrained
( save_directorypush_to_hub: bool = False**kwargs )
Parameters
save_directory (str or os.PathLike) — Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist).
push_to_hub (bool, optional, defaults to False) — Whether or not to push your model to the BOINC AI model hub after saving it. You can specify the repository you want to push to with repo_id (will default to the name of save_directory in your namespace).
batch_decode
( *args**kwargs )
decode
( *args**kwargs )
( config )
Parameters
forward
Parameters
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
1 for tokens that are not masked,
0 for tokens that are masked.
output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
Returns
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
Example:
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TrOCR architecture. Taken from the .
This model was contributed by . The original code can be found .
The quickest way to get started with TrOCR is by checking the , which show how to use the model at inference time as well as fine-tuning on custom data.
TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results on both printed (e.g. the and handwritten (e.g. the text recognition tasks. For more information, see the .
TrOCR is always used within the framework.
A blog post on with TrOCR.
A blog post on how to with TrOCR.
A notebook on how to .
A notebook on and Gradio demo.
A notebook on using native PyTorch.
A notebook on .
task guide.
An interactive-demo on .
TrOCR’s VisionEncoderDecoder model accepts images as input and makes use of to autoregressively generate text given the input image.
The [ViTImageProcessor/DeiTImageProcessor] class is responsible for preprocessing the input image and [RobertaTokenizer/XLMRobertaTokenizer] decodes the generated target tokens to the target string. The wraps [ViTImageProcessor/DeiTImageProcessor] and [RobertaTokenizer/XLMRobertaTokenizer] into a single instance to both extract the input features and decode the predicted token ids.
See the to look for TrOCR checkpoints.
vocab_size (int, optional, defaults to 50265) — Vocabulary size of the TrOCR model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling .
decoder_layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the decoder. See the [LayerDrop paper](see ) for more details.
This is the configuration class to store the configuration of a . It is used to instantiate an TrOCR model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the TrOCR architecture.
Configuration objects inherit from and can be used to control the model outputs. Read the documentation from for more information.
offers all the functionalities of [ViTImageProcessor/DeiTImageProcessor] and [RobertaTokenizer/XLMRobertaTokenizer]. See the and for more information.
a path to a directory containing a feature extractor file saved using the method, e.g., ./my_model_directory/.
a path or url to a saved feature extractor JSON file, e.g., ./my_model_directory/preprocessor_config.json. **kwargs — Additional keyword arguments passed along to both and ~tokenization_utils_base.PreTrainedTokenizer.from_pretrained.
This class method is simply calling the feature extractor , image processor and the tokenizer ~tokenization_utils_base.PreTrainedTokenizer.from_pretrained methods. Please refer to the docstrings of the methods above for more information.
kwargs (Dict[str, Any], optional) — Additional key word arguments passed along to the method.
Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it can be reloaded using the method.
This class method is simply calling and . Please refer to the docstrings of the methods above for more information.
This method forwards all its arguments to TrOCRTokenizer’s . Please refer to the docstring of this method for more information.
This method forwards all its arguments to TrOCRTokenizer’s . Please refer to the docstring of this method for more information.
config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.
The TrOCR Decoder with a language modeling head. Can be used as the decoder part of and VisionEncoderDecoder. This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.FloatTensor] = Noneencoder_attention_mask: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.Tensor] = Nonecross_attn_head_mask: typing.Optional[torch.Tensor] = Nonepast_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)
Indices can be obtained using . See and for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.
or tuple(torch.FloatTensor)
A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.
