But it achieves comparable performance with the supervised model when decoding lines with multiple witnesses, since the model could further benefit from jointly aligning and voting among multiple inputs.
with the multi-input attention mechanism introduced in §3.3, we first generate a high-quality consensus correction for each OCR'd line with witnesses via the correction model trained on synthetic data.