Summary of Text Change Detection in Multilingual Documents Using Image Comparison, by Doyoung Park et al.
Text Change Detection in Multilingual Documents Using Image Comparison
by Doyoung Park, Naresh Reddy Yarram, Sunjin Kim, Minkyu Kim, Seongho Cho, Taehee Lee
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to document comparison called text change detection (TCD) that uses an image comparison model tailored for multilingual documents. Unlike traditional optical character recognition (OCR) methods, TCD employs word-level text image-to-image comparison to detect changes between source and target documents. The model generates bidirectional change segmentation maps and utilizes correlations among multi-scale attention features to enhance performance without requiring explicit text alignment or scaling preprocessing. The approach is evaluated using a benchmark dataset comprising actual printed and scanned word pairs in various languages, as well as public benchmarks Distorted Document Images and the LRDE Document Binarization Dataset. The proposed model outperforms state-of-the-art semantic segmentation and change detection models, as well as conventional OCR-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding changes between different versions of documents. Usually, we use computers to read words from documents, but this can be tricky when the documents are in different languages. To fix this problem, scientists came up with a new way to compare documents that doesn’t rely on computers reading the text. Instead, it compares images of the text at the word level. This helps detect changes between different versions of documents more accurately. The scientists also created a special dataset to test their method and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Alignment » Attention » Semantic segmentation
