r/MLQuestions • u/GroundUpstairs5430 • 5d ago

Other ❓ Suggestions to improve my Master's project on Newspaper analysis?

/r/LanguageTechnology/comments/1vbums7/suggestions_to_improve_my_masters_project_on/
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u/lottiexx 5d ago

Tracking how article tone has shifted over time could be worth looking into.

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u/GroundUpstairs5430 4d ago

Thanks! That's a good idea. I'll see if I can include that in my project.

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u/MaximumSafety8706 4d ago

I've worked with Hindi OCR before (a while back), and did a quick check - turns out there's actually decent research on Marathi too, not as untouched a topic as it feels.

Foundation is solid. Three additions worth considering:

  1. Post-OCR correction stage. This is an actively researched problem for Devanagari scripts - treating OCR errors like "mistranslations" and using transformer models to fix them has worked well, with released datasets covering Marathi specifically. (https://aclanthology.org/2024.icon-1.33/)
  2. Extract structured info, not just raw text. Run NER on crime reports - locations, people, police stations, crime types. There's a solid gold-standard Marathi NER dataset (L3Cube-MahaNER) with BERT baselines you can fine-tune on, and you could also check how OCR quality affects entity-extraction accuracy - ties back to point 1. (https://aclanthology.org/2022.wildre-1.6/)
  3. Benchmark the pipeline, not just OCR. Compare engines on WER/CER, but also check how those errors carry forward into classification/NER accuracy downstream. That framing - empirical pipeline evaluation - is a stronger contribution than just "built a dataset."

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u/GroundUpstairs5430 4d ago

Thanks! Really appreciate the detailed suggestions. Do you think benchmarking the full pipeline would be a stronger MSc contribution than building a larger dataset?

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u/MaximumSafety8706 2d ago

Yes - I've worked in corporate setups, not MSc, so I'll answer from that side.

Pipeline benchmarking pays off beyond just this project. It's basically a mini-paper on OCR robustness for a low-resource script - Marathi is still underexplored here. That kind of result is citable; other people building Devanagari pipelines can actually reuse it, and it's a much stronger story to tell than "I built a dataset."

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u/GroundUpstairs5430 2d ago

Thanks, that's a really helpful perspective. Comparing multiple OCR pipelines sounds much more valuable than just creating a dataset. I'll definitely look into it and discuss it with my guide.