English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We investigate how to train a high quality optical character recognition(OCR) model for difficult historical typefaces on degraded paper. Throughextensive grid searches, we obtain a neural network architecture and a set ofoptimal data augmentation settings. We discuss the influence of factors such asbinarization, input line height, network width, network depth, and othernetwork training parameters such as dropout. Implementing these findings into apractical model, we are able to obtain a 0.44% character error rate (CER) modelfrom only 10,000 lines of training data, outperforming currently availablepretrained models that were trained on more than 20 times the amount of data.We show ablations for all components of our training pipeline, which relies onthe open source framework Calamari.

On the Accuracy of CRNNs for Line-Based OCR: A Multi-Parameter Evaluation