Telugu Document Image Dataset

Home » Dataset Download » Telugu Document Image Dataset

Telugu Document Image Dataset

Datasets

File

Telugu Document Image Dataset

Use Case

Telugu Document Image Dataset

Description

Explore a dataset of 20,000 Telugu document image-text pairs designed for OCR model training. Generate scalable, customizable datasets for text extraction tasks.

Description:

This dataset consists of 20,000 image-text pairs designed to aid in training machine learning models capable of extracting text from scanned Telugu documents. The images in this collection resemble “scans” of documents or book pages, paired with their corresponding text sequences. This dataset aims to reduce the necessity for complex pre-processing steps, such as bounding-box creation and manual text labeling, allowing models to directly map from image inputs to textual sequences.

The main objective is to train models to handle real-world scans, particularly those from aged or damaged documents, without needing to design elaborate computer vision algorithms. The dataset focuses on minimizing the manual overhead involved in traditional document processing methods, making it a valuable resource for tasks like optical character recognition (OCR) in low-resource languages like Telugu.

Download Dataset

Key Features:

Wide Variety of Realistic Scans: The dataset includes images mimicking realistic variations, such as aging effects, smudges, and incomplete characters, commonly found in physical book scans or older documents.
Image-Text Pairing: Each image is linked with its corresponding pure text sequence, allowing models to learn efficient text extraction without additional manual preprocessing steps.
Customizable Data Generation: The dataset is built using open-source generator code, which provides flexibility for users to adjust hundreds of parameters. It supports custom corpora, so users can replace the probabilistically generated “gibberish” text with actual texts relevant to their use cases.
Scalable and Efficient: Thanks to parallelized processing, larger datasets can be generated rapidly. Users with powerful computational resources can expand the dataset size to hundreds of thousands or even millions of pairs, making it an adaptable resource for large-scale AI training.
Multi-Script Support: The generator code can easily be extended to other scripts, including different Indic languages or even non-Indic languages, by modifying the Unicode character set and adjusting parameters such as sentence structure and paragraph lengths.

Dataset Applications:

This dataset is especially useful for developing OCR systems that handle Telugu language documents. However, the data generation process is flexible enough to extend to other Indic languages and non-Indic scripts, making it a versatile resource for cross-lingual and multi-modal research in text extraction, document understanding, and AI-driven translation.