
TL;DR
This paper presents Handwritten augmentation, a data-driven technique for enhancing handwritten character image datasets by altering character shapes, improving CNN-based OCR model performance.
Contribution
It introduces a novel handwritten augmentation method that focuses on shape alterations, easily integrates with existing augmentations, and improves OCR accuracy.
Findings
Enhanced OCR model performance with handwritten augmentation
Compatible with common augmentation techniques like cropping and rotating
Simple to implement and data-driven
Abstract
In this paper, we introduce Handwritten augmentation, a new data augmentation for handwritten character images. This method focuses on augmenting handwritten image data by altering the shape of input characters in training. The proposed handwritten augmentation is similar to position augmentation, color augmentation for images but a deeper focus on handwritten characters. Handwritten augmentation is data-driven, easy to implement, and can be integrated with CNN-based optical character recognition models. Handwritten augmentation can be implemented along with commonly used data augmentation techniques such as cropping, rotating, and yields better performance of models for handwritten image datasets developed using optical character recognition methods.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Hand Gesture Recognition Systems · 3D Surveying and Cultural Heritage
MethodsHandwritten OCR augmentation · Focus
