Summary of Japoc: Japanese Post-ocr Correction Benchmark Using Vouchers, by Masato Fujitake
JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers
by Masato Fujitake
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 A novel study evaluates the performance of error correction methods for Japanese vouchers in Optical Character Recognition (OCR) systems, focusing on improving automation processing by correcting erroneous OCR results. The research fills a gap in existing publicly available benchmarks and methods for Japanese OCR error correction. A proposed post-OCR correction benchmark is developed to measure text recognition accuracy using existing services on Japanese vouchers. Additionally, simple baselines are introduced for error correction using language models and experimentally verified for effectiveness. The study demonstrates that the proposed method can significantly improve overall recognition accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates benchmarks and tests how well methods correct errors in recognizing Japanese voucher texts, like company names on invoices. It’s important to get this right because noisy scans can make it tricky. Right now, there are no good publicly available tools for correcting OCR mistakes in Japanese, so the study proposes new ways to do this using language models. The results show that these methods can greatly improve recognition accuracy. |
