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Scholars in the humanities rely heavily on ancient manuscripts to studyhistory, religion, and socio-political structures in the past. Many effortshave been devoted to digitizing these precious manuscripts using OCRtechnology, but most manuscripts were blemished over the centuries so that anOptical Character Recognition (OCR) program cannot be expected to capture fadedgraphs and stains on pages. This work presents a neural spelling correctionmodel built on Google OCR-ed Tibetan Manuscripts to auto-correct OCR-ed noisyoutput. This paper is divided into four sections: dataset, model architecture,training and analysis. First, we feature-engineered our raw Tibetan etextcorpus into two sets of structured data frames -- a set of paired toy data anda set of paired real data. Then, we implemented a Confidence Score mechanisminto the Transformer architecture to perform spelling correction tasks.According to the Loss and Character Error Rate, our Transformer + Confidencescore mechanism architecture proves to be superior to Transformer, LSTM-2-LSTMand GRU-2-GRU architectures. Finally, to examine the robustness of our model,we analyzed erroneous tokens, visualized Attention and Self-Attention heatmapsin our model.

Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts