Improving OCR for Historical Texts of Multiple Languages
This work addresses OCR challenges for historical documents across languages, but appears incremental as it applies existing methods to specific datasets.
The paper tackled OCR for historical texts in multiple languages by applying deep learning methods to three tasks: improving Hebrew Dead Sea Scrolls recognition with Kraken/TrOCR models, analyzing 16th-18th century meeting resolutions with a CRNN-DeepLabV3+-LSTM hybrid, and recognizing modern English handwriting with a CRNN-ResNet34 model. No concrete performance numbers were reported in the abstract.
This paper presents our methodology and findings from three tasks across Optical Character Recognition (OCR) and Document Layout Analysis using advanced deep learning techniques. First, for the historical Hebrew fragments of the Dead Sea Scrolls, we enhanced our dataset through extensive data augmentation and employed the Kraken and TrOCR models to improve character recognition. In our analysis of 16th to 18th-century meeting resolutions task, we utilized a Convolutional Recurrent Neural Network (CRNN) that integrated DeepLabV3+ for semantic segmentation with a Bidirectional LSTM, incorporating confidence-based pseudolabeling to refine our model. Finally, for modern English handwriting recognition task, we applied a CRNN with a ResNet34 encoder, trained using the Connectionist Temporal Classification (CTC) loss function to effectively capture sequential dependencies. This report offers valuable insights and suggests potential directions for future research.