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import ast
import argparse
import glob
import pickle
import gradio as gr
import numpy as np
import pandas as pd
block_css = """
#notice_markdown {
font-size: 104%
}
#notice_markdown th {
display: none;
}
#notice_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_markdown {
font-size: 104%
}
#leaderboard_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_dataframe td {
line-height: 0.1em;
font-size: 8px;
}
footer {
display:none !important
}
.image-container {
display: flex;
align-items: center;
padding: 1px;
}
.image-container img {
margin: 0 30px;
height: 20px;
max-height: 100%;
width: auto;
max-width: 20%;
}
"""
def model_hyperlink(model_name, link):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
lines = open(filename).readlines()
heads = [v.strip() for v in lines[0].split(",")]
rows = []
for i in range(1, len(lines)):
row = [v.strip() for v in lines[i].split(",")]
for j in range(len(heads)):
item = {}
for h, v in zip(heads, row):
if h != "Model" and h != "Link" and h != "Language Model" and h != "Open Source":
item[h] = float(v)
else:
item[h] = v
if add_hyperlink:
item["Model"] = model_hyperlink(item["Model"], item["Link"])
rows.append(item)
return rows
def get_arena_table(model_table_df):
# sort by rating
model_table_df = model_table_df.sort_values(by=["Average Score"], ascending=False)
values = []
for i in range(len(model_table_df)):
row = []
model_key = model_table_df.index[i]
model_name = model_table_df["Model"].values[model_key]
# rank
row.append(i + 1)
# model display name
row.append(model_name)
# row.append(
# model_table_df["Language Model"].values[model_key]
# )
row.append(
model_table_df["Open Source"].values[model_key]
)
row.append(
model_table_df["Text Recognition"].values[model_key]
)
row.append(
model_table_df["Text Referring"].values[model_key]
)
row.append(
model_table_df["Text Spotting"].values[model_key]
)
row.append(
model_table_df["Relation Extraction"].values[model_key]
)
row.append(
model_table_df["Element Parsing"].values[model_key]
)
row.append(
model_table_df["Mathematical Calculation"].values[model_key]
)
row.append(
model_table_df["Visual Text Understanding"].values[model_key]
)
row.append(
model_table_df["Knowledge Reasoning"].values[model_key]
)
row.append(
model_table_df["Average Score"].values[model_key]
)
values.append(row)
return values
def get_cn_table(model_table_df):
# sort by rating
model_table_df = model_table_df.sort_values(by=["Average Score"], ascending=False)
values = []
for i in range(len(model_table_df)):
row = []
model_key = model_table_df.index[i]
model_name = model_table_df["Model"].values[model_key]
# rank
row.append(i + 1)
# model display name
row.append(model_name)
row.append(
model_table_df["Open Source"].values[model_key]
)
row.append(
model_table_df["Text Recognition"].values[model_key]
)
row.append(
model_table_df["Relation Extraction"].values[model_key]
)
row.append(
model_table_df["Element Parsing"].values[model_key]
)
row.append(
model_table_df["Visual Text Understanding"].values[model_key]
)
row.append(
model_table_df["Knowledge Reasoning"].values[model_key]
)
row.append(
model_table_df["Average Score"].values[model_key]
)
values.append(row)
return values
def build_leaderboard_tab(leaderboard_table_file_en, leaderboard_table_file_cn, show_plot=False):
if leaderboard_table_file_en:
data_en = load_leaderboard_table_csv(leaderboard_table_file_en)
data_cn = load_leaderboard_table_csv(leaderboard_table_file_cn)
model_table_df_en = pd.DataFrame(data_en)
model_table_df_cn = pd.DataFrame(data_cn)
md_head = f"""
# 🏆 OCRBench v2 Leaderboard
| [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR) |
"""
gr.Markdown(md_head, elem_id="leaderboard_markdown")
with gr.Tabs() as tabs:
# arena table
with gr.Tab("OCRBench v2 English subsets", id=0):
arena_table_vals = get_arena_table(model_table_df_en)
md = "OCRBench v2 is a large-scale bilingual text-centric benchmark with currently the most comprehensive set of tasks (4× more tasks than the previous multi-scene benchmark OCRBench), the widest coverage of scenarios (31 diverse scenarios including street scene, receipt, formula, diagram, and so on), and thorough evaluation metrics, with a total of 10, 000 human-verified question-answering pairs and a high proportion of difficult samples."
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"Name",
"Open Source",
"Text Recognition",
"Text Referring",
"Text Spotting",
"Relation Extraction",
"Element Parsing",
"Mathematical Calculation",
"Visual Text Understanding",
"Knowledge Reasoning",
"Average Score",
],
datatype=[
"str",
"markdown",
"str",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
column_widths=[90, 150, 120, 170, 150, 150, 150, 150, 170, 170, 150, 150],
wrap=True,
)
with gr.Tab("OCRBench v2 Chinese subsets", id=1):
arena_table_vals = get_cn_table(model_table_df_cn)
md = "OCRBench v2 is a large-scale bilingual text-centric benchmark with currently the most comprehensive set of tasks (4× more tasks than the previous multi-scene benchmark OCRBench), the widest coverage of scenarios (31 diverse scenarios including street scene, receipt, formula, diagram, and so on), and thorough evaluation metrics, with a total of 10, 000 human-verified question-answering pairs and a high proportion of difficult samples."
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"Name",
"Open Source",
"Text Recognition",
"Relation Extraction",
"Element Parsing",
"Visual Text Understanding",
"Knowledge Reasoning",
"Average Score",
],
datatype=[
"str",
"markdown",
"str",
"number",
"number",
"number",
"number",
"number",
"number",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
# height=700,
column_widths=[60, 120,100, 110, 110, 110, 110, 110, 80],
wrap=True,
)
else:
pass
md_tail = f"""
# Notice
Sometimes, API calls to closed-source models may not succeed. In such cases, we will repeat the calls for unsuccessful samples until it becomes impossible to obtain a successful response.
If you would like to include your model in the OCRBench leaderboard, please follow the evaluation instructions provided on [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR) and feel free to contact us via email at [email protected]. We will update the leaderboard in time."""
gr.Markdown(md_tail, elem_id="leaderboard_markdown")
def build_demo(leaderboard_table_file_en, leaderboard_table_file_cn):
text_size = gr.themes.sizes.text_lg
with gr.Blocks(
title="OCRBench Leaderboard",
theme=gr.themes.Base(text_size=text_size),
css=block_css,
) as demo:
leader_components = build_leaderboard_tab(
leaderboard_table_file_en, leaderboard_table_file_cn, show_plot=True
)
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true")
parser.add_argument("--OCRBench_file_en", type=str, default="./OCRBench_en.csv")
parser.add_argument("--OCRBench_file_cn", type=str, default="./OCRBench_cn.csv")
args = parser.parse_args()
demo = build_demo(args.OCRBench_file_en, args.OCRBench_file_cn)
demo.launch() |