FC-Detection

Flowchart Component Detection Dataset

FC-Detection: Flowchart Component Detection Dataset

Dataset Summary

FC-Detection is a specialized computer vision dataset designed for flowchart component detection and analysis. The dataset contains 99 annotated images of flowcharts with detailed bounding box annotations for 9 different flowchart components, including structural elements (shapes) and directional flow indicators (arrows and endpoints).

This dataset is particularly valuable for document analysis, automated flowchart digitization, workflow understanding, and business process automation applications. The dataset was specifically created and used to train models for the research paper "Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding" (Omasa et al., 2025).

Dataset Structure

Data Splits

The dataset is organized into two splits following COCO annotation format:

Split	Images	Annotations	Avg. Annotations per Image
Train	69	3,951	~57.3
Test	30	1,875	~62.5
Total	99	5,826	~58.8

File Structure

FC-Detection/
├── data_coco_format_start_end_train/
│   ├── annotations/
│   │   └── instances_custom.json    # COCO format annotations
│   └── images/                      # Training images (69 files)
├── data_coco_format_start_end_test/
│   ├── annotations/
│   │   └── instances_custom.json    # COCO format annotations
│   └── images/                      # Test images (30 files)
└── README.md

Annotation Categories

The dataset detects 9 distinct flowchart components:

Category ID	Name	Description	Train Annotations	Test Annotations
1	text	Text labels and content within flowchart elements	967	468
2	arrow	Complete arrow connectors between elements	765	362
3	terminator	Start/end nodes (oval/rounded rectangles)	120	60
4	data	Data input/output shapes (parallelograms)	68	22
5	process	Processing steps (rectangles)	364	182
6	decision	Decision nodes (diamonds)	143	67
7	connection	Connection points or junctions	18	6
8	arrow_start	Arrow starting points/origins	758	356
9	arrow_end	Arrow ending points/destinations	748	352

Key Features

Detailed Arrow Endpoint Detection

The dataset provides granular annotation of arrow components:

Complete arrows: Full arrow connectors
Arrow start points: Precise origin locations
Arrow end points: Precise destination locations

This level of detail enables:

Precise connection analysis between flowchart elements
Better understanding of flow direction in diagrams
More accurate relationship mapping between components
Enhanced arrow tracking in complex flowcharts

Standard Flowchart Elements

Comprehensive coverage of common flowchart shapes:

Terminators: Start/end points of processes
Processes: Action or processing steps
Decisions: Conditional branching points
Data: Input/output operations
Connections: Junction points
Text: Labels and descriptions

Data Format

Annotation Format

The dataset follows the COCO (Common Objects in Context) annotation format:

Images: Metadata including ID, filename, width, height
Categories: 9 predefined flowchart component types
Annotations: Bounding box annotations with:
- id: Unique annotation identifier
- image_id: Reference to parent image
- category_id: Object type (1-9)
- bbox: Bounding box in [x, y, width, height] format
- area: Bounding box area in pixels
- segmentation: Empty (bounding box detection only)
- iscrowd: Always 0 (individual objects)

Image Formats

PNG (primary format)
WEBP
JPEG/JPG
Variable resolutions from small diagrams to large complex flowcharts

Usage Example

from datasets import load_dataset
import json
from PIL import Image

# Load the dataset
dataset = load_dataset("galirage/FC-Detection")

# Access train and test splits
train_split = dataset["train"]
test_split = dataset["test"]

# Load COCO annotations manually
with open("data_coco_format_start_end_train/annotations/instances_custom.json", "r") as f:
    train_annotations = json.load(f)

# Categories mapping
categories = {
    1: "text", 2: "arrow", 3: "terminator", 4: "data", 5: "process",
    6: "decision", 7: "connection", 8: "arrow_start", 9: "arrow_end"
}

# Example: Process first training image
first_image = train_annotations["images"][0]
print(f"Image: {first_image['file_name']}, Size: {first_image['width']}x{first_image['height']}")

# Get annotations for this image
image_annotations = [ann for ann in train_annotations["annotations"] if ann["image_id"] == first_image["id"]]
print(f"Number of annotations: {len(image_annotations)}")

Use Cases

Research Applications

Arrow-Guided VLM Research: Used to train detection models for the "Arrow-Guided VLM" paper, enabling enhanced flowchart understanding through arrow direction encoding
Vision-Language Models: Training object detection components for VLM-based flowchart interpretation systems
Flowchart Topology Understanding: Research in understanding directional flow and node relationships in diagrams

Practical Applications

Document Digitization: Automatic conversion of hand-drawn or scanned flowcharts
Process Mining: Analysis of business process flows
Educational Tools: Automated assessment of flowchart assignments
Workflow Management: Integration with business process management systems
AI Training: Training object detection models for diagram analysis
Research: Computer vision research in document understanding

Dataset Statistics

Total Images: 99
Total Annotations: 5,826
Average Annotations per Image: ~58.8
Most Common Category: Text (1,435 total annotations)
Least Common Category: Connection (24 total annotations)
Dataset Size: ~6.78 MB

Licensing and Citation

This dataset is released under the Apache 2.0 License.

Related Research

This dataset was created to support the research paper:

"Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding" Authors: Takamitsu Omasa, Ryo Koshihara, Masumi Morishige arXiv:2505.07864 (2025)

Dataset Created by: Galirage Inc.
Contact: For questions or issues, please open an issue on the dataset repository.
Research Paper: https://arxiv.org/abs/2505.07864

image imagewidth (px) 141 1.68k