Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks
- The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps - detection and classification - are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from theThe automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps - detection and classification - are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28% was attained on real data obtained from a written exam.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/8296 | Bibliografische Angaben |
Title (English): | Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks |
Conference: | International TRIZ Future Conference (23. : September 12-14, 2023 : Offenburg, Germany) |
Author: | Hartwig GrabowskiStaff MemberGND |
Edition: | 1. |
Year of Publication: | 2023 |
Date of first Publication: | 2023/08/24 |
Place of publication: | Cham |
Publisher: | Springer |
First Page: | 81 |
Last Page: | 94 |
Parent Title (English): | Towards AI-Aided Invention and Innovation : 23rd International TRIZ Future Conference, TFC 2023, Offenburg, Germany, September 12–14, 2023, Proceedings |
Editor: | Denis Cavallucci, Pavel Livotov, Stelian Brad |
Volume: | IFIPAICT 682 |
ISBN: | 978-3-031-42531-8 (Hardcover) |
ISBN: | 978-3-031-42534-9 (Softcover) |
ISBN: | 978-3-031-42532-5 (eBook) |
ISSN: | 1868-4238 |
ISSN: | 1868-422X (E-ISSN) |
DOI: | https://doi.org/10.1007/978-3-031-42532-5_6 |
URL: | https://link.springer.com/chapter/10.1007/978-3-031-42532-5_6 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Institutes: | Bibliografie |
DDC classes: | 000 Allgemeines, Informatik, Informationswissenschaft |
GND Keyword: | Handschrift; Künstliche Intelligenz; Optische Zeichenerkennung |
Tag: | Deep Neural Network; Handswritten Character Recognition; Yolov5 | Formale Angaben |
Relevance: | Konferenzbeitrag: h5-Index < 30 |
Open Access: | Closed |
Licence (German): | Urheberrechtlich geschützt |