Datasets:
Tasks:
Visual Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
Tags:
table-question-answering
table-understanding
markup-driven-understanding
visual-document-understanding
File size: 2,048 Bytes
2d67c36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple
@dataclass(frozen=True)
class CellStruct:
id: str
bbox: Tuple[int, int, int, int] # (x1, y1, x2, y2)
bbox_norm: Tuple[float, float, float, float]
row: int
col: int
text: Optional[str] = None
value: Optional[float] = None
is_highlight: bool = False
confidence: Optional[float] = None
def to_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"bbox": [int(self.bbox[0]), int(self.bbox[1]), int(self.bbox[2]), int(self.bbox[3])],
"bbox_norm": [
float(self.bbox_norm[0]),
float(self.bbox_norm[1]),
float(self.bbox_norm[2]),
float(self.bbox_norm[3]),
],
"row": int(self.row),
"col": int(self.col),
"text": self.text,
"value": self.value,
"is_highlight": bool(self.is_highlight),
"confidence": self.confidence,
}
@dataclass(frozen=True)
class TableStruct:
image_path: str
image_size: Tuple[int, int] # (W, H)
n_rows: int
n_cols: int
cells: List[CellStruct]
table_bbox: Optional[Tuple[int, int, int, int]] = None
confidence: Optional[float] = None
def to_dict(self) -> Dict[str, Any]:
out: Dict[str, Any] = {
"image_path": self.image_path,
"image_size": [int(self.image_size[0]), int(self.image_size[1])],
"n_rows": int(self.n_rows),
"n_cols": int(self.n_cols),
"cells": [c.to_dict() for c in self.cells],
}
if self.table_bbox is not None:
out["table_bbox"] = [
int(self.table_bbox[0]),
int(self.table_bbox[1]),
int(self.table_bbox[2]),
int(self.table_bbox[3]),
]
if self.confidence is not None:
out["confidence"] = float(self.confidence)
return out
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