More Web Proxy on the site http://driver.im/

short-paper

HTR-Flor++: A Handwritten Text Recognition System Based on a Pipeline of Optical and Language Models

Authors:

Arthur Flor de Sousa Neto,

Byron Leite Dantas Bezerra,

Alejandro Héctor Toselli,

Estanislau Baptista LimaAuthors Info & Claims

DocEng '20: Proceedings of the ACM Symposium on Document Engineering 2020

Article No.: 17, Pages 1 - 4

https://doi.org/10.1145/3395027.3419603

Published: 29 September 2020 Publication History

Abstract

Offline Handwritten Text Recognition (HTR) is a task that offers a challenge in computer vision, where images are the only source of information. In fact, several approaches to optical models have been developed, such as through of Hidden Markov Model (HMM) or recurrent Bidirectional/Multidimensional layers. The current state-of-the-art consists of combined deep learning techniques, the Convolutional Recurrent Neural Networks (CRNN), in which recurrent layers still suffer from vanishing gradient problem when processing very long texts. In a way, high-performance models generally have millions of trainable parameters and a high computational cost. However, recently a new optical model architecture, Gated-CNN, demonstrated improvements to complement CRNN modeling. Thus, in this work, we present a new small architecture for HTR (based on Gated-CNN) integrated with two steps of language model at the character and word levels, respectively. Therefore, we used 9 state-of-the-art approaches and validated the results using the IAM public dataset. Finally, the proposed model surpasses the results obtained by different approaches in the literature, reaching recognition rates of CER 2.7% and WER 5.6%, which means an improvement of 13% over the best results on IAM dataset.

References

[1]

T. Bluche and R. Messina. 2017. Gated Convolutional Recurrent Neural Networks for Multilingual Handwriting Recognition. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 01 (11 2017), 646--651.

[2]

D. Castro, B. Bezerra, and M. Valenca. 2018. Boosting the Deep Multidimensional Long-Short-Term Memory Network for Handwritten Recognition Systems. In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, Niagara Falls, USA, 127--132.

[3]

K.-N. Chen, C.-H. Chen, and C.-C. Chang. 2012. Efficient illumination compensation techniques for text images. Digital Signal Processing 22, 5 (2012), 726--733.

Digital Library

[4]

K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. Association for Computational Linguistics, Doha, Qatar, 103--111.

[5]

Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier. 2017. Language Modeling with Gated Convolutional Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML'17). JMLR.org, Sydney, NSW, Australia, 933--941.

[6]

P. Doetsch, M. Kozielski, and H. Ney. 2014. Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition. Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2014 (12 2014), 279--284.

[7]

K. He, X. Zhang, S. Ren, and J. Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, Las Condes, Chile, 1026--1034.

[8]

R. R. Ingle, Y. Fujii, T. Deselaers, J. Baccash, and A. C. Popat. 2019. A Scalable Handwritten Text Recognition System. 2019 International Conference on Document Analysis and Recognition (ICDAR) 01 (2019), 17--24.

[9]

S. Ioffe. 2017. Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 1942--1950.

[10]

M. Kozielski, P. Doetsch, and H. Ney. 2013. Improvements in RWTH's System for Off-Line Handwriting Recognition. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. IEEE, Washington, USA, 935--939.

[11]

U.-V. Marti and H. Bunke. 2002. The IAM-database: An English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5 (11 2002), 39--46.

[12]

V. Pham, T. Bluche, C. Kermorvant, and J. Louradour. 2014. Dropout Improves Recurrent Neural Networks for Handwriting Recognition. In 2014 14th International Conference on Frontiers in Handwriting Recognition. IEEE, Crete Island, Greece, 285--290.

[13]

A. Poznanski and L. Wolf. 2016. CNN-N-Gram for Handwriting Word Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 01 (2016), 2305--2314.

[14]

J. Puigcerver. 2017. Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition? 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 01 (11 2017), 67--72.

[15]

A. Vinciarelli and J. Luettin. 2001. A new normalization technique for cursive handwritten words. Pattern Recognition Letters 22 (2001), 1043--1050.

[16]

P. Voigtlaender, P. Doetsch, and H. Ney. 2016. Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks. In 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, Shenzhen, China, 228--233.

Cited By

Mechi OMehri MIngold REssoukri Ben Amara N(2024)Recognizing text lines in handwritten archival document images using octave convolutional and attention recurrent neural networksMultimedia Tools and Applications10.1007/s11042-024-19717-4Online publication date: 9-Jul-2024
https://doi.org/10.1007/s11042-024-19717-4
Tarride SSchneider YGenerali-Lince MBoillet MAbadie BKermorvant C(2024)Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source LibraryDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70549-6_23(387-404)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70549-6_23
Neto ABezerra BAraújo SSouza WAlves KOliveira MLins SHazin HRocha PToselli A(2024)BRESSAY: A Brazilian Portuguese Dataset for Offline Handwritten Text RecognitionDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70536-6_19(315-333)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70536-6_19
Show More Cited By

Index Terms

HTR-Flor++: A Handwritten Text Recognition System Based on a Pipeline of Optical and Language Models
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

A robust handwritten recognition system for learning on different data restriction scenarios
Highlights
- A Gated-CNN-BGRU optical model for offline Handwritten Text Recognition systems.
Abstract
Handwritten Text Recognition (HTR) systems have gained interest in fields of academic research and commercial applications. Deep learning techniques, and more precisely Convolutional Neural Networks (CNNs), have enabled many recent ...
Offline HWR Accuracy Enhancement with Image Enhancement and Deep Learning Techniques
Abstract
Handwriting recognition (HWR) is the ability of a machine to recognize handwritten text present in images, scanned or photographed. While much of the work reported in this field deals with scanned images, recognizing handwritten content from ...
Handwritten Urdu character recognition using one-dimensional BLSTM classifier

The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DocEng '20: Proceedings of the ACM Symposium on Document Engineering 2020

September 2020

130 pages

ISBN:9781450380003

DOI:10.1145/3395027

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

In-Cooperation

SIGDOC: ACM Special Interest Group for Design of Communications

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 September 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico

Conference

DocEng '20

Sponsor:

SIGWEB

DocEng '20: ACM Symposium on Document Engineering 2020

September 29 - October 1, 2020

CA, Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 194 of 564 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
540
Total Downloads

Downloads (Last 12 months)28
Downloads (Last 6 weeks)2

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mechi OMehri MIngold REssoukri Ben Amara N(2024)Recognizing text lines in handwritten archival document images using octave convolutional and attention recurrent neural networksMultimedia Tools and Applications10.1007/s11042-024-19717-4Online publication date: 9-Jul-2024
https://doi.org/10.1007/s11042-024-19717-4
Tarride SSchneider YGenerali-Lince MBoillet MAbadie BKermorvant C(2024)Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source LibraryDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70549-6_23(387-404)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70549-6_23
Neto ABezerra BAraújo SSouza WAlves KOliveira MLins SHazin HRocha PToselli A(2024)BRESSAY: A Brazilian Portuguese Dataset for Offline Handwritten Text RecognitionDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70536-6_19(315-333)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70536-6_19
Siglidis IGonthier NGaubil JMonnier TAubry M(2024)The Learnable Typewriter: A Generative Approach to Text AnalysisDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70536-6_18(297-314)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-3-031-70536-6_18
Petitpierre RKramer MRappo L(2023)An end-to-end pipeline for historical censuses processingInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-023-00428-926:4(419-432)Online publication date: 17-Mar-2023
https://doi.org/10.1007/s10032-023-00428-9
Hamdan MChaudhary HBali ACheriet M(2022)Refocus attention span networks for handwriting line recognitionInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-022-00422-7Online publication date: 25-Dec-2022
https://doi.org/10.1007/s10032-022-00422-7
Burdett EFujimoto SBrown TShurtz ASegrera DSorenson LClement MPrice J(2022)Active Transfer Learning for Handwriting RecognitionFrontiers in Handwriting Recognition10.1007/978-3-031-21648-0_17(245-258)Online publication date: 4-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-21648-0_17
Wang JSi SHong ZQu XZhu XXiao J(2022)Case Study of Few-Shot Learning in Text Recognition ModelsWeb Information Systems Engineering – WISE 202110.1007/978-3-030-91560-5_29(394-401)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-91560-5_29
Tsochatzidis LSymeonidis SPapazoglou APratikakis I(2021)HTR for Greek Historical Handwritten DocumentsJournal of Imaging10.3390/jimaging71202607:12(260)Online publication date: 2-Dec-2021
https://doi.org/10.3390/jimaging7120260
Aradillas JMurillo-Fuentes JOlmos P(2021)Boosting Offline Handwritten Text Recognition in Historical Documents With Few Labeled LinesIEEE Access10.1109/ACCESS.2021.30826899(76674-76688)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3082689

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents