Recognition of Handwritten Characters Using Google Fonts and Freeman Chain Codes

Abstract : In this study, a unique dataset of a scanned seventeenth-century manuscript is presented which up to now has never been read or analysed. The aim of this research is to be able to transcribe this dataset into machine readable text. The approach used in this study is able to convert the document image without any prior knowledge of the text. In fact, the training set used in this study is a synthetic dataset built on the Google Fonts database. A feed forward Deep Neural Network is trained on a set of different features extracted from the Google Font character images. Well established features such as ratio of character width and height as well as pixel count and Freeman Chain Code is used, with the latter being normalised using Fast Fourier Normalisation that has yielded excellent results in other areas but never been used in Handwritten Character Recognition. In fact, the final results show that this particular Freeman Chain Code feature normalisation yielded the best results achieving an accuracy of 55.1% which is three times higher then the standard Freeman Chain Code normalisation method.
Complete list of metadatas

Cited literature [20 references]  Display  Hide  Download

https://hal.inria.fr/hal-02060035
Contributor : Hal Ifip <>
Submitted on : Thursday, March 7, 2019 - 10:35:58 AM
Last modification on : Friday, March 8, 2019 - 1:23:52 AM
Long-term archiving on: Sunday, June 9, 2019 - 10:17:30 AM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Alexiei Dingli, Mark Bugeja, Dylan Seychell, Simon Mercieca. Recognition of Handwritten Characters Using Google Fonts and Freeman Chain Codes. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.65-78, ⟨10.1007/978-3-319-99740-7_5⟩. ⟨hal-02060035⟩

Share

Metrics

Record views

49