Brno Mobile OCR Dataset Article Swipe
Martin Kišš
,
Michal Hradiš
,
Oldřich Kodym
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1907.01307
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1907.01307
We introduce the Brno Mobile OCR Dataset (B-MOD) for document Optical Character Recognition from low-quality images captured by handheld mobile devices. While OCR of high-quality scanned documents is a mature field where many commercial tools are available, and large datasets of text in the wild exist, no existing datasets can be used to develop and test document OCR methods robust to non-uniform lighting, image blur, strong noise, built-in denoising, sharpening, compression and other artifacts present in many photographs from mobile devices. This dataset contains 2 113 unique pages from random scientific papers, which were photographed by multiple people using 23 different mobile devices. The resulting 19 728 photographs of various visual quality are accompanied by precise positions and text annotations of 500k text lines. We further provide an evaluation methodology, including an evaluation server and a testset with non-public annotations. We provide a state-of-the-art text recognition baseline build on convolutional and recurrent neural networks trained with Connectionist Temporal Classification loss. This baseline achieves 2 %, 22 % and 73 % word error rates on easy, medium and hard parts of the dataset, respectively, confirming that the dataset is challenging. The presented dataset will enable future development and evaluation of document analysis for low-quality images. It is primarily intended for line-level text recognition, and can be further used for line localization, layout analysis, image restoration and text binarization.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1907.01307
- https://arxiv.org/pdf/1907.01307
- OA Status
- green
- References
- 19
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W2955862533Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1907.01307Digital Object Identifier
- Title
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Brno Mobile OCR DatasetWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
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2019-07-02Full publication date if available
- Authors
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Martin Kišš, Michal Hradiš, Oldřich KodymList of authors in order
- Landing page
-
https://arxiv.org/abs/1907.01307Publisher landing page
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https://arxiv.org/pdf/1907.01307Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1907.01307Direct OA link when available
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Computer science, Optical character recognition, Artificial intelligence, Mobile device, Convolutional neural network, Line (geometry), Field (mathematics), Pattern recognition (psychology), Information retrieval, Image (mathematics), Computer vision, World Wide Web, Geometry, Pure mathematics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.parts | 178 |
| abstract_inverted_index.rates | 172 |
| abstract_inverted_index.tools | 34 |
| abstract_inverted_index.using | 98 |
| abstract_inverted_index.where | 31 |
| abstract_inverted_index.which | 92 |
| abstract_inverted_index.Mobile | 4 |
| abstract_inverted_index.enable | 193 |
| abstract_inverted_index.exist, | 45 |
| abstract_inverted_index.future | 194 |
| abstract_inverted_index.images | 15 |
| abstract_inverted_index.layout | 220 |
| abstract_inverted_index.lines. | 123 |
| abstract_inverted_index.mature | 29 |
| abstract_inverted_index.medium | 175 |
| abstract_inverted_index.mobile | 19, 79, 101 |
| abstract_inverted_index.neural | 152 |
| abstract_inverted_index.noise, | 66 |
| abstract_inverted_index.people | 97 |
| abstract_inverted_index.random | 89 |
| abstract_inverted_index.robust | 59 |
| abstract_inverted_index.server | 133 |
| abstract_inverted_index.strong | 65 |
| abstract_inverted_index.unique | 86 |
| abstract_inverted_index.visual | 110 |
| abstract_inverted_index.(B-MOD) | 7 |
| abstract_inverted_index.Dataset | 6 |
| abstract_inverted_index.Optical | 10 |
| abstract_inverted_index.dataset | 82, 186, 191 |
| abstract_inverted_index.develop | 53 |
| abstract_inverted_index.further | 125, 215 |
| abstract_inverted_index.images. | 203 |
| abstract_inverted_index.methods | 58 |
| abstract_inverted_index.papers, | 91 |
| abstract_inverted_index.precise | 115 |
| abstract_inverted_index.present | 74 |
| abstract_inverted_index.provide | 126, 141 |
| abstract_inverted_index.quality | 111 |
| abstract_inverted_index.scanned | 25 |
| abstract_inverted_index.testset | 136 |
| abstract_inverted_index.trained | 154 |
| abstract_inverted_index.various | 109 |
| abstract_inverted_index.Temporal | 157 |
| abstract_inverted_index.achieves | 162 |
| abstract_inverted_index.analysis | 200 |
| abstract_inverted_index.baseline | 146, 161 |
| abstract_inverted_index.built-in | 67 |
| abstract_inverted_index.captured | 16 |
| abstract_inverted_index.contains | 83 |
| abstract_inverted_index.dataset, | 181 |
| abstract_inverted_index.datasets | 39, 48 |
| abstract_inverted_index.devices. | 20, 80, 102 |
| abstract_inverted_index.document | 9, 56, 199 |
| abstract_inverted_index.existing | 47 |
| abstract_inverted_index.handheld | 18 |
| abstract_inverted_index.intended | 207 |
| abstract_inverted_index.multiple | 96 |
| abstract_inverted_index.networks | 153 |
| abstract_inverted_index.Character | 11 |
| abstract_inverted_index.analysis, | 221 |
| abstract_inverted_index.artifacts | 73 |
| abstract_inverted_index.different | 100 |
| abstract_inverted_index.documents | 26 |
| abstract_inverted_index.including | 130 |
| abstract_inverted_index.introduce | 1 |
| abstract_inverted_index.lighting, | 62 |
| abstract_inverted_index.positions | 116 |
| abstract_inverted_index.presented | 190 |
| abstract_inverted_index.primarily | 206 |
| abstract_inverted_index.recurrent | 151 |
| abstract_inverted_index.resulting | 104 |
| abstract_inverted_index.available, | 36 |
| abstract_inverted_index.commercial | 33 |
| abstract_inverted_index.confirming | 183 |
| abstract_inverted_index.denoising, | 68 |
| abstract_inverted_index.evaluation | 128, 132, 197 |
| abstract_inverted_index.line-level | 209 |
| abstract_inverted_index.non-public | 138 |
| abstract_inverted_index.scientific | 90 |
| abstract_inverted_index.Recognition | 12 |
| abstract_inverted_index.accompanied | 113 |
| abstract_inverted_index.annotations | 119 |
| abstract_inverted_index.compression | 70 |
| abstract_inverted_index.development | 195 |
| abstract_inverted_index.low-quality | 14, 202 |
| abstract_inverted_index.non-uniform | 61 |
| abstract_inverted_index.photographs | 77, 107 |
| abstract_inverted_index.recognition | 145 |
| abstract_inverted_index.restoration | 223 |
| abstract_inverted_index.sharpening, | 69 |
| abstract_inverted_index.annotations. | 139 |
| abstract_inverted_index.challenging. | 188 |
| abstract_inverted_index.high-quality | 24 |
| abstract_inverted_index.methodology, | 129 |
| abstract_inverted_index.photographed | 94 |
| abstract_inverted_index.recognition, | 211 |
| abstract_inverted_index.Connectionist | 156 |
| abstract_inverted_index.binarization. | 226 |
| abstract_inverted_index.convolutional | 149 |
| abstract_inverted_index.localization, | 219 |
| abstract_inverted_index.respectively, | 182 |
| abstract_inverted_index.Classification | 158 |
| abstract_inverted_index.state-of-the-art | 143 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |