Pattern spotting in historical documents using convolutional models

Ignacio Úbeda; Jose M. Saavedra; Stéphane Nicolas; Caroline Petitjean; Laurent Heutte

doi:10.1145/3352631.3352645

Pattern spotting in historical documents using convolutional models

Ignacio Úbeda
, Jose M. Saavedra
, Stéphane Nicolas
, Caroline Petitjean
, Laurent Heutte

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

7 Citas (Scopus)

Resumen

Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query. Contrary to object detection, no prior information nor predefined class is given about the query so training a model of the object is not feasible. In this paper, a convolutional neural network approach is proposed to tackle this problem. We use RetinaNet as a feature extractor to obtain multiscale embeddings of the regions of the documents and also for the queries. Experiments conducted on the DocExplore dataset show that our proposal is better at locating patterns and requires less storage for indexing images than the state-of-the-art system, but fails at retrieving multiple pages containing instances of the query.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019
Editorial	Association for Computing Machinery
Páginas	60-65
Número de páginas	6
ISBN (versión digital)	9781450376686
DOI	https://doi.org/10.1145/3352631.3352645
Estado	Publicada - 20 sep. 2019
Publicado de forma externa	Sí
Evento	5th International Workshop on Historical Document Imaging and Processing, HIP 2019, held in conjunction with ICDAR 2019 - Sydney, Australia Duración: 20 sep. 2019 → 21 sep. 2019

Serie de la publicación

Nombre	ACM International Conference Proceeding Series

Conferencia o congreso

Conferencia o congreso	5th International Workshop on Historical Document Imaging and Processing, HIP 2019, held in conjunction with ICDAR 2019
País/Territorio	Australia
Ciudad	Sydney
Período	20/09/19 → 21/09/19

Nota bibliográfica

Publisher Copyright:
© 2019 Association for Computing Machinery.

Acceder al documento

10.1145/3352631.3352645

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Úbeda, I., Saavedra, J. M., Nicolas, S., Petitjean, C., & Heutte, L. (2019). Pattern spotting in historical documents using convolutional models. En Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019 (pp. 60-65). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3352631.3352645

@inproceedings{8b205c82942d4f5798dab97206c78719,

title = "Pattern spotting in historical documents using convolutional models",

abstract = "Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query. Contrary to object detection, no prior information nor predefined class is given about the query so training a model of the object is not feasible. In this paper, a convolutional neural network approach is proposed to tackle this problem. We use RetinaNet as a feature extractor to obtain multiscale embeddings of the regions of the documents and also for the queries. Experiments conducted on the DocExplore dataset show that our proposal is better at locating patterns and requires less storage for indexing images than the state-of-the-art system, but fails at retrieving multiple pages containing instances of the query.",

keywords = "Convolutional neural network, Historical documents, Image retrieval, Pattern spotting",

author = "Ignacio {\'U}beda and Saavedra, \{Jose M.\} and St{\'e}phane Nicolas and Caroline Petitjean and Laurent Heutte",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 5th International Workshop on Historical Document Imaging and Processing, HIP 2019, held in conjunction with ICDAR 2019 ; Conference date: 20-09-2019 Through 21-09-2019",

year = "2019",

month = sep,

day = "20",

doi = "10.1145/3352631.3352645",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "60--65",

booktitle = "Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019",

}

Úbeda, I, Saavedra, JM, Nicolas, S, Petitjean, C & Heutte, L 2019, Pattern spotting in historical documents using convolutional models. En Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 60-65, 5th International Workshop on Historical Document Imaging and Processing, HIP 2019, held in conjunction with ICDAR 2019, Sydney, Australia, 20/09/19. https://doi.org/10.1145/3352631.3352645

Pattern spotting in historical documents using convolutional models. / Úbeda, Ignacio; Saavedra, Jose M.; Nicolas, Stéphane et al.
Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019. Association for Computing Machinery, 2019. p. 60-65 (ACM International Conference Proceeding Series).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Pattern spotting in historical documents using convolutional models

AU - Úbeda, Ignacio

AU - Saavedra, Jose M.

AU - Nicolas, Stéphane

AU - Petitjean, Caroline

AU - Heutte, Laurent

PY - 2019/9/20

Y1 - 2019/9/20

N2 - Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query. Contrary to object detection, no prior information nor predefined class is given about the query so training a model of the object is not feasible. In this paper, a convolutional neural network approach is proposed to tackle this problem. We use RetinaNet as a feature extractor to obtain multiscale embeddings of the regions of the documents and also for the queries. Experiments conducted on the DocExplore dataset show that our proposal is better at locating patterns and requires less storage for indexing images than the state-of-the-art system, but fails at retrieving multiple pages containing instances of the query.

AB - Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query. Contrary to object detection, no prior information nor predefined class is given about the query so training a model of the object is not feasible. In this paper, a convolutional neural network approach is proposed to tackle this problem. We use RetinaNet as a feature extractor to obtain multiscale embeddings of the regions of the documents and also for the queries. Experiments conducted on the DocExplore dataset show that our proposal is better at locating patterns and requires less storage for indexing images than the state-of-the-art system, but fails at retrieving multiple pages containing instances of the query.

KW - Convolutional neural network

KW - Historical documents

KW - Image retrieval

KW - Pattern spotting

UR - https://www.scopus.com/pages/publications/85074773138

U2 - 10.1145/3352631.3352645

DO - 10.1145/3352631.3352645

M3 - Conference contribution

AN - SCOPUS:85074773138

T3 - ACM International Conference Proceeding Series

SP - 60

EP - 65

BT - Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019

PB - Association for Computing Machinery

T2 - 5th International Workshop on Historical Document Imaging and Processing, HIP 2019, held in conjunction with ICDAR 2019

Y2 - 20 September 2019 through 21 September 2019

ER -

Pattern spotting in historical documents using convolutional models

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