Datasets:
metadata
language:
- en
pretty_name: 'Comics: Pick-A-Panel'
tags:
- comics
dataset_info:
- config_name: char_coherence
features:
- name: sample_id
dtype: string
- name: context
sequence: image
- name: options
sequence: image
- name: index
dtype: int32
- name: solution_index
dtype: int32
- name: split
dtype: string
- name: task_type
dtype: string
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 379249617
num_examples: 143
download_size: 379268925
dataset_size: 379249617
- config_name: sequence_filling
features:
- name: sample_id
dtype: string
- name: context
sequence: image
- name: options
sequence: image
- name: index
dtype: int32
- name: solution_index
dtype: int32
- name: split
dtype: string
- name: task_type
dtype: string
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 1230082746
num_examples: 262
download_size: 1153097954
dataset_size: 1230082746
- config_name: text_closure
features:
- name: sample_id
dtype: string
- name: context
sequence: image
- name: options
sequence: image
- name: index
dtype: int32
- name: solution_index
dtype: int32
- name: split
dtype: string
- name: task_type
dtype: string
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 952974973
num_examples: 274
download_size: 930660064
dataset_size: 952974973
configs:
- config_name: char_coherence
data_files:
- split: val
path: char_coherence/val-*
- config_name: sequence_filling
data_files:
- split: val
path: sequence_filling/val-*
- config_name: text_closure
data_files:
- split: val
path: text_closure/val-*
Comics: Pick-A-Panel
This is the dataset for the ICDAR 2025 Competition on Comics Understanding in the Era of Foundational Models
The dataset contains five subtask or skills:
Sequence Filling
Task Description
Given a sequence of comic panels, a missing panel, and a set of option panels, the task is to select the panel that best fits the sequence.Character Coherence, Visual Closure, Text Closure
Task Description
These skills require understanding the context sequence to then pick the best panel to continue the story, focusing on the characters, the visual elements, and the text:
- Character Coherence: Given a sequence of comic panels, pick the panel from the two options that best continues the story in a coherent with the characters. Both options are the same panel, but the text in the speech bubbles is has been swapped.
- Visual Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the visual elements.
- Text Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the text. All options are the same panel, but with text in the speech retrieved from different panels.
Caption Relevance
Task Description
Given a caption from the previous panel, select the panel that best continues the story.Loading the Data
from datasets import load_dataset
skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
split = "val" # "val", "test"
dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split)
Map to single images
If your model can only process single images, you can render each sample as a single image:
from PIL import Image, ImageDraw, ImageFont
import numpy as np
from datasets import Features, Value, Image as ImageFeature
class SingleImagePickAPanel:
def __init__(self, max_size=500, margin=10, label_space=20, font_path="Arial.ttf"):
self.max_size = max_size
self.margin = margin
self.label_space = label_space
# Add separate font sizes
self.label_font_size = 20
self.number_font_size = 24
self.font_path = font_path
def resize_image(self, img):
"""Resize image keeping aspect ratio if longest edge > max_size"""
if max(img.size) > self.max_size:
ratio = self.max_size / max(img.size)
new_size = tuple(int(dim * ratio) for dim in img.size)
return img.resize(new_size, Image.Resampling.LANCZOS)
return img
def create_mask_panel(self, width, height):
"""Create a question mark panel"""
mask_panel = Image.new("RGB", (width, height), (200, 200, 200))
draw = ImageDraw.Draw(mask_panel)
font_size = int(height * 0.8)
try:
font = ImageFont.truetype(self.font_path, font_size)
except:
raise ValueError("Font file not found")
text = "?"
bbox = draw.textbbox((0, 0), text, font=font)
text_x = (width - (bbox[2] - bbox[0])) // 2
text_y = (height - (bbox[3] - bbox[1])) // 2
draw.text((text_x, text_y), text, fill="black", font=font)
return mask_panel
def draw_number_on_panel(self, panel, number, font):
"""Draw number on the bottom of the panel with background"""
draw = ImageDraw.Draw(panel)
# Get text size
bbox = draw.textbbox((0, 0), str(number), font=font)
text_width = bbox[2] - bbox[0]
text_height = bbox[3] - bbox[1]
# Calculate position (bottom-right corner)
padding = 2
text_x = panel.size[0] - text_width - padding
text_y = panel.size[1] - text_height - padding
# Draw semi-transparent background
bg_rect = [(text_x - padding, text_y - padding),
(text_x + text_width + padding, text_y + text_height + padding)]
draw.rectangle(bg_rect, fill=(255, 255, 255, 180))
# Draw text
draw.text((text_x, text_y), str(number), fill="black", font=font)
return panel
def map_to_single_image(self, examples):
"""Process a batch of examples from a HuggingFace dataset"""
single_images = []
for i in range(len(examples['sample_id'])):
# Get context and options for current example
context = examples['context'][i] if len(examples['context'][i]) > 0 else []
options = examples['options'][i]
# Resize all images
context = [self.resize_image(img) for img in context]
options = [self.resize_image(img) for img in options]
# Calculate common panel size (use median size to avoid outliers)
all_panels = context + options
if len(all_panels) > 0:
widths = [img.size[0] for img in all_panels]
heights = [img.size[1] for img in all_panels]
panel_width = int(np.median(widths))
panel_height = int(np.median(heights))
# Resize all panels to common size
context = [img.resize((panel_width, panel_height)) for img in context]
options = [img.resize((panel_width, panel_height)) for img in options]
# Create mask panel for sequence filling tasks if needed
if 'index' in examples and len(context) > 0:
mask_idx = examples['index'][i]
mask_panel = self.create_mask_panel(panel_width, panel_height)
context.insert(mask_idx, mask_panel)
# Calculate canvas dimensions based on whether we have context
if len(context) > 0:
context_row_width = panel_width * len(context) + self.margin * (len(context) - 1)
options_row_width = panel_width * len(options) + self.margin * (len(options) - 1)
canvas_width = max(context_row_width, options_row_width)
canvas_height = (panel_height * 2 +
self.label_space * 2)
else:
# Only options row for caption_relevance
canvas_width = panel_width * len(options) + self.margin * (len(options) - 1)
canvas_height = (panel_height +
self.label_space)
# Create canvas
final_image = Image.new("RGB", (canvas_width, canvas_height), "white")
draw = ImageDraw.Draw(final_image)
try:
label_font = ImageFont.truetype(self.font_path, self.label_font_size)
number_font = ImageFont.truetype(self.font_path, self.number_font_size)
except:
raise ValueError("Font file not found")
current_y = 0
# Add context section if it exists
if len(context) > 0:
# Draw "Context" label
bbox = draw.textbbox((0, 0), "Context", font=label_font)
text_x = (canvas_width - (bbox[2] - bbox[0])) // 2
draw.text((text_x, current_y), "Context", fill="black", font=label_font)
current_y += self.label_space
# Paste context panels
x_offset = (canvas_width - (panel_width * len(context) +
self.margin * (len(context) - 1))) // 2
for panel in context:
final_image.paste(panel, (x_offset, current_y))
x_offset += panel_width + self.margin
current_y += panel_height
# Add "Options" label
bbox = draw.textbbox((0, 0), "Options", font=label_font)
text_x = (canvas_width - (bbox[2] - bbox[0])) // 2
draw.text((text_x, current_y), "Options", fill="black", font=label_font)
current_y += self.label_space
# Paste options with numbers on panels
x_offset = (canvas_width - (panel_width * len(options) +
self.margin * (len(options) - 1))) // 2
for idx, panel in enumerate(options):
# Create a copy of the panel to draw on
panel_with_number = panel.copy()
if panel_with_number.mode != 'RGBA':
panel_with_number = panel_with_number.convert('RGBA')
# Draw number on panel
panel_with_number = self.draw_number_on_panel(
panel_with_number,
idx,
number_font
)
# Paste the panel with number
final_image.paste(panel_with_number, (x_offset, current_y), panel_with_number)
x_offset += panel_width + self.margin
# Convert final_image to PIL Image format (instead of numpy array)
single_images.append(final_image)
# Prepare batch output
examples['single_image'] = single_images
return examples
from datasets import load_dataset
skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
split = "val" # "val", "test"
dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split)
processor = SingleImagePickAPanel()
dataset = dataset.map(
processor.map_to_single_image,
batched=True,
batch_size=32,
remove_columns=['context', 'options']
)
dataset.save_to_disk(f"ComPAP_{skill}_{split}_single_images")
Summit Results and Leaderboard
The competition is hosted in the Robust Reading Competition website and the leaderboard is available here.
Citation
coming soon