Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit
Table 4: Performance in terms of CRR, WRR for Google OCR
['[BOLD] Model', '[BOLD] Bhagavad Gītā Ins', '[BOLD] Bhagavad Gītā Del', '[BOLD] Bhagavad Gītā Sub', '[BOLD] Sahaśranāma Ins', '[BOLD] Sahaśranāma Del', '[BOLD] Sahaśranāma Sub', '[BOLD] System errors Ins', '[BOLD] System errors Del', '[BOLD] System errors Sub']
, we analyse the reduction in specific error types for PCRF and CopyNet after the alignment of the predicted string with that of the ground truth in terms of insertion, deletion and substitution. We also report the system induced errors, where a correct component at the input (OCR output) is mispredicted to a wrong output by the model. CopyNet outperforms PCRF in correcting the errors and it also introduces lesser number of errors of its own. Both CopyNet and PCRF Schnober et al. Both the systems perform well in handling substitution errors, the type which dominated the strings in OCRTest, though neither of the systems was able to correct the insertion errors. Insertion can be seen as a special case of 1-to-many insertion matches, which both systems are ideally capable of handling. We see that for Sahaśranāma, CopyNet corrects about 17.24 % of the deletion errors as against <5% of the deletion errors corrected by PCRF.