Enhancing text recognition on Tor Darknet images
| Autor: | Mhd Wesam Al-Nabki, Deisy Chaves, Enrique Alegre, Pablo Blanco Medina, Eduardo Fidalgo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Text Spotting
Cybersecurity Artificial neural network Computer science Plain text business.industry Darknet ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Text recognition computer.file_format Spotting Pipeline (software) Task (project management) OCR Tor darknet ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Text Recognition Artificial intelligence business computer Complement (set theory) |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname |
| DOI: | 10.17979/spudc.9788497497169.828 |
| Popis: | [Abstract] Text Spotting can be used as an approach to retrieve information found in images that cannot be obtained otherwise, by performing text detection rst and then recognizing the located text. Examples of images to apply this task on can be found in Tor network images, which contain information that may not be found in plain text. When comparing both stages, the latter performs worse due to the low resolution of the cropped areas among other problems. Focusing on the recognition part of the pipeline, we study the performance of ve recognition approaches, based on state-ofthe- art neural network models, standalone OCR, and OCR enhancements. We complement them using string-matching techniques with two lexicons and compare computational time on ve di erent datasets, including Tor network images. Our nal proposal achieved 39,70% precision of text recognition in a custom dataset of images taken from Tor domains |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|