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vision_config (`dict` or [`SLANeXtVisionConfig`], *optional*):
Configuration for the vision encoder. If `None`, a default [`SLANeXtVisionConfig`] is used.
post_conv_in_channels (`int`, *optional*, defaults to 256):
Number of input channels for the post-encoder convolution layer.
post_conv_out_channels (`int`, *optional*, defaults to 512):
Number of output channels for the post-encoder convolution layer.
out_channels (`int`, *optional*, defaults to 50):
Vocabulary size for the table structure token prediction head, i.e., the number of distinct structure
tokens the model can predict.
hidden_size (`int`, *optional*, defaults to 512):
Dimensionality of the hidden states in the attention GRU cell and the structure/location prediction heads.
max_text_length (`int`, *optional*, defaults to 500):
Maximum number of autoregressive decoding steps (tokens) for the structure and location decoder.
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head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Initialize the decoder vocabulary for table structure recognition.
Builds a character dictionary mapping HTML table structure tokens (e.g., ``, `
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`"eos"` (end-of-sequence) tokens. Merged `
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when applicable.
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Post-process the raw model outputs to decode the predicted table structure into an HTML token sequence.
Converts the model's predicted probability distributions over the structure vocabulary into a sequence of
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` tags to form a complete HTML table structure.
Args:
outputs ([`SLANeXtForTableRecognitionOutput`]):
Raw outputs from the SLANeXt model. The `last_hidden_state` field contains the predicted probability
distributions over the structure vocabulary at each decoding step, with shape
`(batch_size, max_text_length, num_classes)`.
Returns:
`dict`: A dictionary containing:
- **structure** (`list[str]`): The predicted HTML table structure as a list of tokens, wrapped with
``, ``, and `
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- **structure_score** (`float`): The mean confidence score across all predicted tokens.
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