Datasets:
metadata
dataset_info:
- config_name: Dhurjati
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- config_name: Gidugu
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- config_name: Gurajada
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- config_name: Mandali-Regular
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- config_name: NotoSerifTelugu-VariableFont_wght
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- config_name: Pothana2000
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- config_name: Ramabhadra1
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- config_name: RamaneeyaWin
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- config_name: Ramaraja-Regular
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- config_name: Suguna
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- config_name: Suranna
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- config_name: Suravara_Samhita
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- config_name: Suravara_Swarna
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- config_name: Suravaram
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- config_name: TLOTAmmaBI_ship
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- config_name: TLOTAmmaB_ship
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- config_name: TLOTAmrutaBI_Ship
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- config_name: TLOTAtreyaBI_Ship
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- config_name: TLOTDevaI_Ship
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- config_name: TLOTMenakaBI_Ship
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path: TLOTKrishnaN_Ship/train-*
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path: TLOTKrishnaN_Ship/validation-*
- split: test
path: TLOTKrishnaN_Ship/test-*
- config_name: TLOTManuBI_Ship
data_files:
- split: train
path: TLOTManuBI_Ship/train-*
- split: validation
path: TLOTManuBI_Ship/validation-*
- split: test
path: TLOTManuBI_Ship/test-*
- config_name: TLOTManuB_Ship
data_files:
- split: train
path: TLOTManuB_Ship/train-*
- split: validation
path: TLOTManuB_Ship/validation-*
- split: test
path: TLOTManuB_Ship/test-*
- config_name: TLOTManuI_Ship
data_files:
- split: train
path: TLOTManuI_Ship/train-*
- split: validation
path: TLOTManuI_Ship/validation-*
- split: test
path: TLOTManuI_Ship/test-*
- config_name: TLOTManuN_Ship
data_files:
- split: train
path: TLOTManuN_Ship/train-*
- split: validation
path: TLOTManuN_Ship/validation-*
- split: test
path: TLOTManuN_Ship/test-*
- config_name: TLOTMenakaBI_Ship
data_files:
- split: train
path: TLOTMenakaBI_Ship/train-*
- split: validation
path: TLOTMenakaBI_Ship/validation-*
- split: test
path: TLOTMenakaBI_Ship/test-*
- config_name: TLOTMenakaB_Ship
data_files:
- split: train
path: TLOTMenakaB_Ship/train-*
- split: validation
path: TLOTMenakaB_Ship/validation-*
- split: test
path: TLOTMenakaB_Ship/test-*
- config_name: TLOTMenakaI_Ship
data_files:
- split: train
path: TLOTMenakaI_Ship/train-*
- split: validation
path: TLOTMenakaI_Ship/validation-*
- split: test
path: TLOTMenakaI_Ship/test-*
- config_name: TLOTMenakaN_Ship
data_files:
- split: train
path: TLOTMenakaN_Ship/train-*
- split: validation
path: TLOTMenakaN_Ship/validation-*
- split: test
path: TLOTMenakaN_Ship/test-*
- config_name: TLOTPavaniBI_Ship
data_files:
- split: train
path: TLOTPavaniBI_Ship/train-*
- split: validation
path: TLOTPavaniBI_Ship/validation-*
- split: test
path: TLOTPavaniBI_Ship/test-*
- config_name: TLOTPavaniB_Ship
data_files:
- split: train
path: TLOTPavaniB_Ship/train-*
- split: validation
path: TLOTPavaniB_Ship/validation-*
- split: test
path: TLOTPavaniB_Ship/test-*
- config_name: TLOTPriyaB_Ship
data_files:
- split: train
path: TLOTPriyaB_Ship/train-*
- split: validation
path: TLOTPriyaB_Ship/validation-*
- split: test
path: TLOTPriyaB_Ship/test-*
- config_name: TLOTRajanBI_Ship
data_files:
- split: train
path: TLOTRajanBI_Ship/train-*
- split: validation
path: TLOTRajanBI_Ship/validation-*
- split: test
path: TLOTRajanBI_Ship/test-*
- config_name: TLOTRajanB_Ship
data_files:
- split: train
path: TLOTRajanB_Ship/train-*
- split: validation
path: TLOTRajanB_Ship/validation-*
- split: test
path: TLOTRajanB_Ship/test-*
- config_name: TLOTRajaniBI_Ship
data_files:
- split: train
path: TLOTRajaniBI_Ship/train-*
- split: validation
path: TLOTRajaniBI_Ship/validation-*
- split: test
path: TLOTRajaniBI_Ship/test-*
- config_name: TLOTRajaniB_Ship
data_files:
- split: train
path: TLOTRajaniB_Ship/train-*
- split: validation
path: TLOTRajaniB_Ship/validation-*
- split: test
path: TLOTRajaniB_Ship/test-*
- config_name: TLOTSanjanaBI_Ship
data_files:
- split: train
path: TLOTSanjanaBI_Ship/train-*
- split: validation
path: TLOTSanjanaBI_Ship/validation-*
- split: test
path: TLOTSanjanaBI_Ship/test-*
- config_name: TLOTSanjanaB_Ship
data_files:
- split: train
path: TLOTSanjanaB_Ship/train-*
- split: validation
path: TLOTSanjanaB_Ship/validation-*
- split: test
path: TLOTSanjanaB_Ship/test-*
- config_name: TLOTSitaraBI_Ship
data_files:
- split: train
path: TLOTSitaraBI_Ship/train-*
- split: validation
path: TLOTSitaraBI_Ship/validation-*
- split: test
path: TLOTSitaraBI_Ship/test-*
- config_name: TLOTSitaraB_Ship
data_files:
- split: train
path: TLOTSitaraB_Ship/train-*
- split: validation
path: TLOTSitaraB_Ship/validation-*
- split: test
path: TLOTSitaraB_Ship/test-*
- config_name: TLOTSwamiBI_Ship
data_files:
- split: train
path: TLOTSwamiBI_Ship/train-*
- split: validation
path: TLOTSwamiBI_Ship/validation-*
- split: test
path: TLOTSwamiBI_Ship/test-*
- config_name: TLOTSwamiB_Ship
data_files:
- split: train
path: TLOTSwamiB_Ship/train-*
- split: validation
path: TLOTSwamiB_Ship/validation-*
- split: test
path: TLOTSwamiB_Ship/test-*
- config_name: TLOTVennela1B_Ship
data_files:
- split: train
path: TLOTVennela1B_Ship/train-*
- split: validation
path: TLOTVennela1B_Ship/validation-*
- split: test
path: TLOTVennela1B_Ship/test-*
- config_name: TLOTVennelaBI_Ship
data_files:
- split: train
path: TLOTVennelaBI_Ship/train-*
- split: validation
path: TLOTVennelaBI_Ship/validation-*
- split: test
path: TLOTVennelaBI_Ship/test-*
- config_name: TLOTVennelaI_Ship
data_files:
- split: train
path: TLOTVennelaI_Ship/train-*
- split: validation
path: TLOTVennelaI_Ship/validation-*
- split: test
path: TLOTVennelaI_Ship/test-*
- config_name: TenaliRamakrishna-Regular
data_files:
- split: train
path: TenaliRamakrishna-Regular/train-*
- split: validation
path: TenaliRamakrishna-Regular/validation-*
- split: test
path: TenaliRamakrishna-Regular/test-*
- config_name: TimmanaRegular
data_files:
- split: train
path: TimmanaRegular/train-*
- split: validation
path: TimmanaRegular/validation-*
- split: test
path: TimmanaRegular/test-*
- config_name: Vanib
data_files:
- split: train
path: Vanib/train-*
- split: validation
path: Vanib/validation-*
- split: test
path: Vanib/test-*
- config_name: Vemana
data_files:
- split: train
path: Vemana/train-*
- split: validation
path: Vemana/validation-*
- split: test
path: Vemana/test-*
- config_name: akshar
data_files:
- split: train
path: akshar/train-*
- split: validation
path: akshar/validation-*
- split: test
path: akshar/test-*
- config_name: gautami
data_files:
- split: train
path: gautami/train-*
- split: validation
path: gautami/validation-*
- split: test
path: gautami/test-*
- config_name: gautamib
data_files:
- split: train
path: gautamib/train-*
- split: validation
path: gautamib/validation-*
- split: test
path: gautamib/test-*
license: gpl-3.0
task_categories:
- image-to-text
language:
- te
size_categories:
- 1M<n<10M
Dataset Card for Dataset Name
Dataset Details
Dataset Description
This extensive dataset, hosted on Huggingface, is a comprehensive resource for Optical Character Recognition (OCR) in the Telugu language, featuring an impressive array of 80+ configurations. Each configuration in this dataset corresponds to a unique font, meticulously curated by Dr. Rakesh Achanta and sourced from his GitHub repository (https://github.com/TeluguOCR/banti_telugu_ocr).
The dataset is specifically designed to support and enhance the development of OCR models, ranging from simple Convolutional Recurrent Neural Network (CRNN) architectures to more advanced systems like trOCR. The versatility of this dataset lies in its large volume and diversity, making it an ideal choice for researchers and developers aiming to build robust OCR systems for the Telugu script.
Key Features:
- Font Diversity: Over 80 unique fonts, each forming a separate configuration, providing a rich variety in text styles and nuances.
- Large Volume: Each configuration contains approximately 800,000 examples, summing up to a vast pool of data for comprehensive training and evaluation.
- Data Split: The dataset is pre-split into training, validation, and test sets, following a 60/20/20 ratio, to facilitate efficient model training and benchmarking.
- Use Cases: Ideal for developing a wide range of OCR models - from basic CRNNs to sophisticated models like trOCR.
- Accessibility: Hosted on Huggingface, ensuring easy access and integration with various machine learning frameworks and tools.
This dataset stands as a testament to Dr. Rakesh Achanta's dedication to enhancing Telugu language processing technologies. It is not just a tool for model development but also a gateway to preserving and digitizing the rich literary heritage of the Telugu language.
Researchers and developers leveraging this dataset are encouraged to adhere to the ethical guidelines of AI research and development, ensuring that the applications developed are for the benefit of language preservation, accessibility, and technological advancement in a responsible manner.
- Fonts Curated by: Dr. Rakesh Achanta
- Shared by: Krishna Sriharsha Gundu
- Language(s) (NLP): Telugu
Dataset Sources [optional]
- Repository: Original Books Dataset