Papers by Maria Koutraki
Neural OCR Post-Hoc Correction of Historical Corpora (2021.tacl-1)
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| Challenge: | Optical character recognition (OCR) is crucial for a deeper access to historical collections. |
| Approach: | They propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct OCR transcription errors. |
| Outcome: | The proposed model reduces the word error rate of 32.3% by more than 89% on a historical book corpus in German language. |