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RAJNOHA, M.; BURGET, R.; DUTTA, M.
Original Title
Handwriting Comenia Script Recognition with Convolutional Neural Network
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
This paper deals with handwriting recognition (HWR) using artificial intelligence of so–called Comenia script - a modern handwritten font similar to block letters recently introduced at primary schools in the Czech Republic. This work describes a method how to extend a limited training set of handwritten letters and proposes a new method to increase stability and accuracy by artificially created image samples. We examined a large set of algorithms including a deep learning method for classification of the handwriting characters. The best results were achieved using a convolutional neural network, which achieved the accuracy or character recognition 90.04%
English abstract
Keywords
CNN; deep learning; handwriting recognition; HWR; OCR
Key words in English
Authors
RIV year
2018
Released
06.07.2017
Location
Barcelona
ISBN
978-1-5090-3981-4
Book
40th Anniversary of International Conference on Telecommunications and Signal Processing (TSP)
Pages from
775
Pages to
779
Pages count
5
URL
https://ieeexplore.ieee.org/document/8076093
BibTex
@inproceedings{BUT137770, author="Martin {Rajnoha} and Radim {Burget} and Malay Kishore {Dutta}", title="Handwriting Comenia Script Recognition with Convolutional Neural Network", booktitle="40th Anniversary of International Conference on Telecommunications and Signal Processing (TSP)", year="2017", pages="775--779", address="Barcelona", doi="10.1109/TSP.2017.8076093", isbn="978-1-5090-3981-4", url="https://ieeexplore.ieee.org/document/8076093" }