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GHCR-A dataset for Grantha handwritten character recognition.

Basaraboyina Yohoshiva1, Nagendra Panini Challa1

  • 1VIT-AP University, Amaravati, Andhra Pradesh, India.

Data in Brief
|September 10, 2024
PubMed
Summary

A new dataset of handwritten Grantha characters, including numbers and vowels, was created to aid machine learning research. This resource addresses a gap in available data for the Grantha script, supporting Indian language technology development.

Keywords:
Deep learningHandwritten character recognitionMachine learningMachine visionOptical recognition systems

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Area of Science:

  • Computer Science
  • Linguistics
  • Digital Humanities

Background:

  • The Grantha script, historically significant in South Indian languages, lacks sufficient digital datasets for research.
  • Existing resources for Grantha character recognition are limited, hindering advancements in related AI applications.

Purpose of the Study:

  • To introduce a comprehensive dataset of handwritten Grantha characters (numbers and vowels).
  • To provide a benchmark resource for developing and evaluating machine learning models for Grantha character recognition.
  • To facilitate research in Indian languages connected to the Grantha script.

Main Methods:

  • Collected handwritten samples of 10 Grantha numbers and 34 vowels from diverse age groups.
  • Digitized and preprocessed 5852 images, including segmentation, resizing, and grayscale conversion.
  • Organized data into image and CSV formats with corresponding labels for machine learning.

Main Results:

  • The final dataset contains 1330 samples for numbers and 4522 samples for vowels.
  • The dataset comprises 44 distinct Grantha characters, with 133 handwritten samples per character.
  • Data is available in both image and CSV formats, suitable for immediate use.

Conclusions:

  • This dataset fills a critical gap for Grantha script research, particularly in numeral and vowel recognition.
  • It serves as a foundational resource for machine learning initiatives focused on Grantha-influenced Indian languages.
  • The availability of this dataset is expected to spur further innovation in script recognition and natural language processing.