CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model
Paper • 2503.06472 • Published • 8
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"山花如绣草如茵",
"士林吾兄正",
"丙寅夏月梁启超"
],
"reference": "家酝满缾书满架，山花如绣草如茵。士林吾兄正，丙寅夏月，梁启超。",
"revised": [
"家酝满缾书满架",
"山花如绣草如茵",
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"丙寅夏月梁启超"
],
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"有基抛却自家无尽藏沿门托",
"钵效贫儿希白仁兄属漱冥"
],
"reference": "无声无臭独知时，此是乾坤万有基。抛却自家无尽藏，沿门托钵效贫儿。希白仁兄属。漱冥。",
"revised": [
"无声无臭独知时此是乾坤万",
"有基抛却自家无尽藏沿门托",
"钵效贫儿希白仁兄属漱冥"
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"群臣则富贵之至于朝",
"廷之礼不可以不肃",
"节临欧帖成亲王"
],
"reference": "汉文帝时，丞相申屠嘉入朝见邓通在上傍，有怠慢礼。嘉进曰：陛下爱幸群臣，则富贵之，至于朝廷之礼，不可以不肃。节临欧帖。成亲王。",
"revised": [
"汉文帝时丞相申屠嘉入",
"朝见邓通在上傍有怠",
"慢礼嘉进曰陛下爱幸",
"群臣则... | |
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"锯儒魁垒馨我胶庠",
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"甲戌夏孝胥"
],
"reference": "微学匡扶寿于旗翼，巨儒魁垒馨我胶庠。醉陶仁兄大雅属，甲戌（1934）夏孝胥。",
"revised": [
"微学匡扶寿于旗翼",
"巨儒魁垒馨我胶庠",
"醉陶仁兄大雅属",
"甲戌（）夏孝胥"
],
"shapes": [
{
"label": "微",
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"raw": [
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"满袖门知何弥健佛光",
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],
"reference": "八月我归来，诗稿携满袖。口口阿弥陀，佛光照大地。《昔游》一首，书为焕新同志正。散耳。",
"revised": [
"八月我归来诗稿携",
"满袖口口阿弥陀佛光",
"照大地昔游一首书为焕新同志正"
],
"shapes": [
{
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"points": [
... | |
10008.jpg | {
"raw": [
"截使江流入瓮顿都海浪净山",
"玉虹飞下到人闲激起雷奔电阁",
"桥乍扬尘走马桥头流水行船天",
"阿锥造鹊谁传织女牛郸千万",
"湛江责年莲沙调寄恶月朴初"
],
"reference": "截使江流入瓮，顿教海浪浮山。玉虹飞下到人间，激起雷奔电闪。桥上扬尘走马，桥头流水行船。天河谁造鹊谁传?织女牛郎千万。湛江青年运河调寄西江月，朴初。",
"revised": [
"截使江流入瓮顿教海浪浮山",
"玉虹飞下到人间激起雷奔电闪",
"桥上扬尘走马桥头... | |
10010.jpg | {
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"兴酣落笔摇五岳",
"诗来笑傲凌沧洲",
"少湘仁兄酷卒艺衡陟闭此联身墉归乙",
"旌才土时季九兔岁"
],
"reference": "兴酣落笔摇五岳，诗成笑傲凌沧海。少湘仁兄酷爱艺术得余此联属题归之。雄才书时年九十岁。",
"revised": [
"兴酣落笔摇五岳",
"诗成笑傲凌沧海",
"少湘仁兄酷爱艺术得余此联属题归之",
"雄才书时年九十岁"
],
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"岁来气请远",
"潮落涛波平",
"黎元洪"
],
"reference": "岚来气清远，潮落涛波平。黎元洪。",
"revised": [
"岚来气清远",
"潮落涛波平",
"黎元洪"
],
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{
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"十年树木百年树人",
"旭庄仁丈大人集句命书",
"杏邨江春霖"
],
"reference": "刚日读经柔日读史，十年树木百年树人。旭庄仁丈大人集句命书，杏村江春霖。",
"revised": [
"刚日读经柔日读史",
"十年树木百年树人",
"旭庄仁丈大人集句命书",
"杏村江春霖"
],
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{
"label": "刚",
... | |
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"raw": [
"开张天岸马",
"音逸人中铁夫龙"
],
"reference": "开张天岸马，奇逸人中龙。铁夫。",
"revised": [
"开张天岸马",
"奇逸人中龙铁夫"
],
"shapes": [
{
"label": "开",
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0.5946,
0.0075
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... | |
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"老松阅世卧云壑",
"乔大如今似画图",
"茂之一兄属",
"涤生曾国藩"
],
"reference": "老松阅世卧云壑，乔木如今似画图。茂之一兄属，涤生曾国藩。",
"revised": [
"老松阅世卧云壑",
"乔木如今似画图",
"茂之一兄属",
"涤生曾国藩"
],
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{
"label": "老",
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... | |
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"渚水出芙崇天然知",
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],
"reference": "清水出芙蓉，天然去雕饰。潮田先生雅正，乙丑（1985年）秋中，新我左笔。",
"revised": [
"清水出芙蓉天然去",
"雕饰潮田先生雅正乙丑（年）秋中"
],
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{
"label": "渚",
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0.5133,
... | |
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"俭钓共",
"癸亥冬独翁",
"德",
"亩波同志知"
],
"reference": "俭为共德。永波同志正，癸亥冬独翁。",
"revised": [
"俭为共",
"癸亥冬独翁",
"德",
"永波同志正"
],
"shapes": [
{
"label": "俭",
"points": [
[
... | |
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"raw": [
"碑经晓凉桐窗露",
"读画精披竹径云",
"丕公先生",
"吴湖帆"
],
"reference": "研经晓滴桐窗露，读画晴披竹径云。正公先生，吴湖帆。",
"revised": [
"研经晓滴桐窗露",
"读画晴披竹径云",
"正公先生",
"吴湖帆"
],
"shapes": [
{
"label": "碑",
"points": [
... | |
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"物",
"有",
"则",
"所",
"传",
"寺岛尊兄大人属",
"少荃李鸿章"
],
"reference": "有物有则所传。南岛道兄大人属，少荃李鸿章。",
"revised": [
"有",
"物",
"有",
"则",
"所",
"传",
"南岛道兄大人属",
"少荃李鸿章"
],
"s... | |
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"raw": [
"辅卅长反莫如德",
"令闻广警施于身",
"息多今月八"
],
"reference": "辅世长民莫如德，令闻广誉施于身。息侯金梁。",
"revised": [
"辅世长民莫如德",
"令闻广誉施于身",
"息侯金梁"
],
"shapes": [
{
"label": "辅",
"points": [
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"raw": [
"细雨鱼儿出征风燕",
"斜千子宏老弟属托水",
"杜甫内"
],
"reference": "细雨鱼儿出，微风燕子斜。杜甫句，日空老弟之属冷月。",
"revised": [
"细雨鱼儿出微风燕",
"甫句日空老弟之属",
"杜甫句"
],
"shapes": [
{
"label": "细",
"points": [
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0.583... | |
1003.jpg | {
"raw": [
"大名驰中外",
"美利尽东南",
"大美号骏发",
"孝脊"
],
"reference": "大名驰中外，美利尽东南。大美号骏发。孝胥。",
"revised": [
"大名驰中外",
"美利尽东南",
"大美号骏发",
"孝胥"
],
"shapes": [
{
"label": "大",
"points": [
[
... | |
10032.jpg | {
"raw": [
"誉头望明月",
"把酒问眚天",
"姚古芥集太白东坡句鹰纯初兼盟之属",
"岁吹焉逢俗佛节第邓众疋"
],
"reference": "举头望明月，把酒问青天。姚古芬集太白、东坡句譍纯初画盟之属。岁次焉逢浴佛节，弟邓尔疋。",
"revised": [
"举头望明月",
"把酒问青天",
"姚古芬集太白东坡句譍纯初画盟之属",
"岁次焉逢浴佛节弟邓尔疋"
],
"shapes": [
{
"lab... |
End of preview. Expand in Data Studio
🧠 CalliReader: Contextualizing Chinese Calligraphy via an Embedding-aligned Vision Language Model
CalliBench is aimed to comprehensively evaluate VLMs' performance on the recognition and understanding of Chinese calligraphy.
📦 Dataset Summary
Samples: 3,192 image–annotation pairs
Tasks: Full-page recognition and Contextual VQA (choice of author/layout/style, bilingual interpretation, and intent analysis).
Annotations:
- Metadata of author, layout, and style.
- Fine-grained annotations of character-wise bounding boxes and labels.
- Certain samples include contextual VQA.
🧪 How To Use
All .parqeut files of different tiers can be found in the sub-folders of data. Pandas can be used to parse and further process those files.
For example, to load a sample and convert its image into a .jpg file:
import pandas as pd
import io
from PIL import Image
df = pd.read_parquet('./data/train.parquet')
image_data = df.iloc[0]['image']
image = Image.open(io.BytesIO(image_data['bytes']))
image.save('output_image.jpg')
🤗 License
Apache 2.0 – open for research and commercial use.
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