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gksriharsha commited on Mar 23

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 ### 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 90+ 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 90 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.

 ### 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.