{"repo":"sparkfish/augraphy","free":true,"listed":false,"github":"https://github.com/sparkfish/augraphy","clone":"git clone https://github.com/sparkfish/augraphy.git","description":"Augmentation pipeline for rendering synthetic paper printing, faxing, scanning and copy machine processes","language":"Python","stars":567,"topics":["data-augmentation","crappification","deep-neural-networks","training-data","machine-learning","data-pipeline","image-processing","augmentation-pipeline","synthetic-data","synthetic-dataset-generation"],"license":"MIT","category":"machine-learning","readme_excerpt":"Augraphy is a Python library that creates multiple copies of original documents though an augmentation pipeline that randomly distorts each copy -- degrading the clean version into dirty and realistic copies rendered through synthetic paper printing, faxing, scanning and copy machine processes. Highly-configurable pipelines apply adjustments to the originals to create realistic old or noisy documents by acting as a factory, producing almost an infinite number of variations from their source. This simulation of realistic paper-oriented process distortions can create large amounts of training data for AI/ML processes to learn how to remove those distortions. Treatments applied by Augraphy fabricate realistic documents that appear to have been printed on dirty laser or inkjet printers, scanned by dirty office scanners, faxed by low-resolution fax machines and otherwise mistreated by real-world paper handling office equipment. What makes Augraphy Magical? https://github.com/user-attachments/assets/09fc8ddc-4475-4f81-9472-6615c3ecd5c6 Virtually no readily available datasets exist with both a clean and noisy version of target documents. Augraphy addresses that problem by manufacturing large volumes of high-quality noisy documents to train alongside their clean source originals. Training neural networks typically requires augmenting limited sources of data in a variety of ways so that networks can learn to generalize their solutions. Networks designed to work with scanned document i","default_branch":null,"files":null,"tree":[],"storefront":"/r/sparkfish","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sparkfish/augraphy/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}