A data augmentations library for audio, image, text, and video.

Geometric Augmentation for Text Image

Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Python implementation of Text-Image-Augmentation

【ICCV 2023】Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning & 【IJCV 2025】Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation

Sparrow: Data-Efficient Video-LLM with Text-to-Image Augmentation

I2T2I: Text-to-Image Synthesis with textual data augmentation

This research aims to fine-tune an Arabic OCR model using Tesseract 5.0, enhancing text recognition accuracy through extensive data collection, preprocessing, and image generation. By leveraging advanced training techniques and data augmentation, we achieve significant improvements in word error rates (WER).

TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models

Tutorial on data augmentations for ML with images and text

FitAug-1.2M: a large-scale image-text augmentation dataset and benchmark resource for VLM-based fitness guidance.

GANs are a deep learning technique that uses two neural networks, a generator and a discriminator, to generate realistic synthetic data by competing against each other. They have applications in image generation, text synthesis, and data augmentation.

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Code Repository for "LAP-GAN: Label augmentation with perceptual loss for self-supervised text-to-image synthesis"

A unified data-ingestion CLI that auto-detects and converts text, image, audio and tabular sources into standardized training datasets with schema validation, sampling, and augmentation capabilities.

Texture augmentation using AR Foundation in UNITY

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Developed a multi-modal data augmentation algorithm that generates new image-text pairsto enhance data efficiency and model performance in visual-language representation learning tasks.

data augmentation for image, text, audio, and sensor

Image augmentation written in golang to create variations in text and image style

A PyTorch implementation of Pix2Pix for converting Persian handwritten text images into typed text. The repository is modular, configurable via command-line arguments, and supports auxiliary losses and data augmentation for improved image-to-image translation performance.

DupliPy is a quick and easy-to-use package that can handle text formatting and data augmentation tasks for NLP in Python. It now offers support for image augmentation tasks as well.

Built an app that generates images from text using Stable Diffusion, PyTorch, and Tkinter, to facilitate, data augmentation, and assist in creative design tasks.

Building an application that generate Image from Text to facilitate data augmentation, and assist in creative design tasks using Stable Diffusion, Tkinter and PyTorch CUDA.

A multipurpose AI chatbot with Gemini, Groq, and OpenRouter integration, featuring real-time conversations, image recognition, voice chat, and collaborative sessions. Supports search augmentation, text-to-speech, and animated visualizations.

Handwritten Text Recognition using CRNN with Profile Normalization and RWGD Augmentation. A complete end-to-end OCR pipeline built in PyTorch for converting handwritten images into digital text, with CER/WER evaluation and memory-efficient training on local systems.

Practicals for Deep Learning related courses (Master MVA) : Image Classification/Regression/Denoising, Text Classification, Deep Reinforcement Learning, Generative Modelling (GAN, VAE), Deep Learning for Small Data (Transfer Learning, Data Augmentation, Semi-Supervised)

This project implements an automated system capable of segmenting and recognizing characters in distorted text-based CAPTCHA images. The complete pipeline includes web scraping, preprocessing, segmentation, data augmentation, and supervised Machine Learning classification.

Build a Generative AI Model from scratch using VAEs and GANs to create synthetic datasets for text, images, and tabular data. Features FastAPI for real-time generation, Streamlit for visualization, and PyTorch for training — ideal for privacy, simulation, and advanced data augmentation use cases.

About Comprehensive ML & DL implementations using PyTorch and scikit-learn, including linear & softmax regression, LDA, SVMs, CNNs, ResNet fine-tuning with augmentation, Transformer image captioning, SimCLR self-supervised learning, and unsupervised methods (K-Means, PCA, GMM) on CIFAR-10, MNIST, and housing data and Text-to-Speech with Piper.