Handwritten Text Recognition using Hybrid CNN-GRU Model and CNN-LSTM Model on Parzival Database: A Novel Approach

DOI

10.2991/978-94-6463-529-4_34How to use a DOI?

Keywords

CNN; HTR; GRU; Parzival; DL; LSTM

Abstract

The use of deep learning models, especially Convolutional Neural Networks and Gated Recurrent Units, has been a popular approach to improve the performance of handwriting recognition (HTR) algorithms In this research, perzival dataset is used it evaluates the performance of the HTR system incorporating CNN and GRU as a reference. Our results show that this integration significantly reduces loss and improves the HTR process, demonstrating the effectiveness of deep learning models used for HTR implementation. This study proposes a new method for handwritten text recognition (HTR) using a hybrid of CNN-GRU and CNN-LSTM models on the Parzival database. CNN-GRU and CNN-LSTM models were used to extract spatial and temporal features from the input images, respectively. The analysis showed that the CNN-GRU model suffered less loss compared to the CNN-LSTM model, indicating better performance. The proposed method provides a promising strategy to improve the accuracy of HTR hybrid modeling, and has the potential to be applied to various applications such as document digitization.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Madhav Sharma
PY  - 2024
DA  - 2024/10/04
TI  - Handwritten Text Recognition using Hybrid CNN-GRU Model and CNN-LSTM Model on Parzival Database: A Novel Approach
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 383
EP  - 390
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_34
DO  - 10.2991/978-94-6463-529-4_34
ID  - Sharma2024
ER  -