Cross Domain Evaluation of Text Detection Models

Adamu Ali-Gombe, Eyad Elyan, Carlos Moreno-García, Chrisina Jayne

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Text detection is a very common task across a wide range of domains, such as document image analysis, remote identity verification, amongst others. It is also considered an integral component of any text recognition system, where the performance of recognition tasks largely depends on the accuracy of the detection of text components. Various text detection models have been developed in the past decade. However, localizing text characters is still considered as one of the most challenging computer vision tasks within the text recognition task. Typical challenges include illumination, font types and sizes, languages, and many others. Furthermore, detection models are often evaluated using specific datasets without much work on cross-datasets and domain evaluation. In this paper, we present an experimental framework to evaluate the generalization capability of state-of-the-art text detection models across different application domains. Extensive experiments were carried using different established methods: EAST, CRAFT, Tessaract and Ensembles applied to various publicly available datasets. The generalisation performance of the models was evaluated and compared using precision, recall and F1-score. This paper opens a future direction in investigating ensemble models for text detection to improve generalisation.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2022 - 31st International Conference on Artificial Neural Networks, 2022, Proceedings
EditorsElias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031159336
Publication statusPublished - 15 Sept 2022
Event31st International Conference on Artificial Neural Networks, ICANN 2022 - Bristol, United Kingdom
Duration: 6 Sept 20229 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13531 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference31st International Conference on Artificial Neural Networks, ICANN 2022
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


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