We are introducing a universal handwritten image augmentation method that is language-agnostic. This groundbreaking technique can be applied to handwritten images in any language worldwide, marking it as the first of its kind. There are four methods for handwritten images which are ThickOCR, ThinOCR, Elongate OCR, Line Erase OCR.

The **Adaptively Sparse Transformer** is a type of [Transformer](https://paperswithcode.com/method/transformer).

Adaptively Sparse Transformer

Adaptively Sparse Transformers

Handwritten OCR

Handwritten image augmentation

**Adaptive Feature Pooling** pools features from all levels for each proposal in object detection and fuses them for the following prediction. For each proposal, we map them to different feature levels. Following the idea of [Mask R-CNN](https://paperswithcode.com/method/adaptive-feature-pooling), [RoIAlign](https://paperswithcode.com/method/roi-align) is used to pool feature grids from each level. Then a fusion operation (element-wise max or sum) is utilized to fuse feature grids from different levels.

The motivation for this technique is that in an [FPN](https://paperswithcode.com/method/fpn) we assign proposals to different feature levels based on the size of proposals, which could be suboptimal if images with small differences are assigned to different levels, or if the importance of features is not strongly correlated to their level which they belong.

Source	Handwritten image augmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com