An Efficient Unsupervised Approach for OCR Error Correction of Vietnamese OCR Text

Different types of OCR errors often occur in texts due to the low quality scanned document images or limitations software. In this paper, we propose a novel unsupervised approach for error correction. Correction candidates are generated and explored their neighborhoods using correction character edits controlled by an adapted hill-climbing algorithm. characters extracted from only original ground truth texts, which do not depend on training data. A weighted objective function used score rank is heuristically tested find optimal weight combinations. The proposed model evaluated text dataset originating Vietnamese handwritten database ICFHR 2018 online recognition competition. also verified concerning its stability complexity. experimental results show that our achieves competitive performance compared other models