Where do I get a data set for Hindi characters recognition?

To obtain datasets for Hindi character recognition, developers have several reliable options. Here are three primary sources and methods:

1. Public Dataset Repositories Kaggle (kaggle.com) hosts user-contributed datasets, including Hindi character collections. For example, the Devanagari Character Dataset provides labeled images of handwritten Hindi characters, suitable for training OCR models. Similarly, the UCI Machine Learning Repository offers structured datasets like Devanagari Handwritten Characters, which includes 92,000 images across 46 classes. These platforms are ideal for developers seeking pre-processed, ready-to-use data.

2. Government and Academic Resources India’s Ministry of Electronics and Information Technology (MeitY) supports initiatives like the Indian Script Data project, which curates multilingual datasets, including Hindi. Academic institutions such as IITs (Indian Institutes of Technology) often publish datasets for research—check their AI/ML departments for accessibility. For instance, IIT Indore’s Hindi Text Recognition Corpus combines scanned documents and annotated text, useful for complex recognition tasks.

3. Synthetic Data Generation If existing datasets lack diversity, tools like SynthText or TRDG (Text Recognition Data Generator) can generate synthetic Hindi text images. These tools allow customization of fonts, backgrounds, and distortions to mimic real-world scenarios. Additionally, Google’s TensorFlow Datasets library includes utilities for augmenting small datasets with transformations like rotation and noise injection.

For practical implementation:

• Validate dataset quality by checking annotation accuracy and class balance.
• Use frameworks like PyTorch or TensorFlow for model training, leveraging pre-trained models (e.g., ResNet) for transfer learning.
• Explore GitHub repositories like CLOVA AI’s DeepText Recognition for reference implementations.

Always verify dataset licenses (e.g., CC-BY, MIT) to ensure compliance with your project’s requirements. For niche use cases, consider collaborating with universities or crowdsourcing platforms like Amazon Mechanical Turk to create custom datasets.

[1] Kaggle [2] UCI Machine Learning Repository [3] Indian Ministry of Electronics and Information Technology [4] TensorFlow Datasets Documentation [5] CLOVA AI GitHub Repository