AnyText: Multilingual Visual Text Generation And Editing
Paper
• 2311.03054 • Published
• 1
Datasets:
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gt_661.jpg | a store window with clothing on display | null | 512 | 512 | |
gt_1154.jpg | two signs on a brick wall that say welcome to our breakfast club | [
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"text": "廉政教学 快捷拿证",
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gt_2494.jpg | a sign that reads julius kuntst, ganghu guanzhong | [
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gt_3847.jpg | a vintage liquor store sign with a red and white background | [
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"text": "LIQUOR",
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gt_5093.jpg | a sign for a temporary tattoo parlor in a building | [
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gt_4488.jpg | baked fresh today sign | [
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gt_226.jpg | a store with chinese writing on the front | [
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"text": "夏季团购量大从优",
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gt_998.jpg | pendergrass flea market | [
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gt_4122.jpg | china owa factory | [
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"text": "郝太太蚕丝",
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gt_3745.jpg | kapil kiset han, kolkata | [
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gt_304.jpg | a sign for a beer pub on a building | [
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"text": "PilsnerUrqueII",
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gt_5518.jpg | a chinese restaurant with a sign has texts | [
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gt_4424.jpg | a tie dye hoodie with the words don't smoke the organic on it | [
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gt_5369.jpg | samuel adams imperial white 750ml | [
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"pos": 4,
"text": "SAMUEL",
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{
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gt_1264.jpg | a wine barrel and two glasses of wine on a table | [
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... | 512 | 512 | |
gt_4427.jpg | embroidered logo psd mockup | [
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... | 512 | 512 | |
gt_3002.jpg | a building with a green sign and chinese writing | [
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"pos": 5,
"text": "关爱自己 美丽健",
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{
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gt_2562.jpg | a car is parked in front of a building with chinese writing on it | [
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gt_4049.jpg | china international trade center | [
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gt_1599.jpg | a sign advertising korean charcoal bbq in a store | [
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gt_5306.jpg | a building with colorful flags and chinese writing on it | [
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gt_1116.jpg | an escalator with a sign that says welcome to easy and fun learning zone | [
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gt_2279.jpg | grand canyon regulations sign | null | 512 | 512 | |
gt_1085.jpg | a white suv parked in front of a restaurant | [
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gt_2.jpg | a building with a sign that says happy bbq | [
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"pos": 4,
"text": "HäppyB哈·贝比",
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] | 512 | 512 | |
gt_484.jpg | shanghai xinmin v elevator components co, ltd | [
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"text": "上海贤奕电梯配件有限公司",
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... | 512 | 512 | |
gt_1417.jpg | the sign for fishermans wharf is on top of a building | [
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... | 512 | 512 | |
gt_5423.jpg | mary shelley - author of frankenstein | [
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gt_3145.jpg | a building with a sign that says chinese union | [
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"pos": 3,
"text": "西农村信用社",
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gt_3970.jpg | beaver tails pub & restaurant | [
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gt_1735.jpg | a display of old books on display in a store | [
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gt_255.jpg | frankford fire department helmet | [
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"pos": 4,
"text": "CHIEF",
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{
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gt_4194.jpg | a button with an orange and white design on it | [
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... | 512 | 512 | |
gt_5234.jpg | a sign on a wall with chinese writing | [
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... | 512 | 512 | |
gt_96.jpg | a large sign with the word hotel on it | [
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gt_4565.jpg | an old book with a portrait of a woman | [
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gt_3522.jpg | the blackboard in the classroom has a picture of people on it | [
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gt_1356.jpg | a sign on a wall with chinese characters and english writing | [
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"text": "上海市长宁区建筑业协会",
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... | 512 | 512 |
End of preview. Expand
in Data Studio
Dataset from AnyText: Multilingual Visual Text Generation And Editing.
Dataset description from Anytext Team:
Currently, there is a relative scarcity of public datasets for text generation tasks, especially those involving non-Latin script languages. To address this, we introduce a large-scale multilingual dataset called AnyWord-3M. The images in this dataset are sourced from Noah-Wukong, LAION-400M, and OCR recognition datasets such as ArT, COCO-Text, RCTW, LSVT, MLT, MTWI, ReCTS, etc. These images cover a variety of scenes containing text, including street views, book covers, advertisements, posters, and movie frames. Apart from OCR datasets that directly utilize annotated information, all other images are processed using the detection and recognition models of PP-OCR. Subsequently, text descriptions are generated using BLIP-2. After rigorous filtering rules and meticulous post-processing, we have obtained a total of 3,034,486 images, containing over 9 million lines of text and more than 20 million characters or Latin script characters. Additionally, we randomly selected 1,000 images from the Wukong and LAION subsets to create the evaluation set AnyText-benchmark, specifically for assessing the accuracy and quality of Chinese and English text generation. The remaining images serve as the training set AnyWord-3M, which includes approximately 1.6 million Chinese images, 1.39 million English images, and 10,000 images in other languages, including Japanese, Korean, Arabic, Bengali, and Hindi. For detailed statistical analysis and randomly selected example images, please refer to our paper AnyText. (Note: The dataset released in this instance is version V1.1)
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Number of rows:
3,533,893