title: Weakly supervised information extraction from inscrutable handwritten
    document images
summary: State-of-the-art information extraction methods are limited by OCR errors.
  They work well for printed text in form-like documents, but unstructured,
  handwritten documents still remain a challenge. Adapting existing models to
  domain-specific training data is quite expensive, because of two factors, 1)
  limited availability of the domain-specific documents (such as handwritten
  prescriptions, lab notes, etc.), and 2) annotations become even more
  challenging as one needs domain-specific knowledge to decode inscrutable
  handwritten document images. In this work, we focus on the complex problem of
  extracting medicine names from handwritten prescriptions using only weakly
  labeled data. The data consists of images along with the list of medicine names
  in it, but not their location in the image. We solve the problem by first
  identifying the regions of interest, i.e., medicine lines from just weak labels
  and then injecting a domain-specific medicine language model learned using only
  synthetically generated data. Compared to off-the-shelf state-of-the-art
  methods, our approach performs >2.5x better in medicine names extraction from
  prescriptions.
paper_authors._id: 6487ca50536e4b60679170d9
paper_authors.name: Sujoy Paul
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-30T08:44:06.626Z
paper_authors._id: 66a8a7968268a5b1a0819668
paper_authors.avatarUrl: /avatars/a8cdca874279beeef1023c8ecb10403a.svg
paper_authors.fullname: Sujoy Paul
paper_authors.type: user
paper_authors.user: sujoyp
paper_authors._id: 6487ca50536e4b60679170da
paper_authors.name: Gagan Madan
paper_authors._id: 6487ca50536e4b60679170db
paper_authors.name: Akankshya Mishra
paper_authors._id: 6487ca50536e4b60679170dc
paper_authors.name: Narayan Hegde
paper_authors._id: 6487ca50536e4b60679170dd
paper_authors.name: Pradeep Kumar
paper_authors._id: 6487ca50536e4b60679170de
paper_authors.name: Gaurav Aggarwal
paper_title: Weakly supervised information extraction from inscrutable handwritten
    document images
paper_summary: State-of-the-art information extraction methods are limited by OCR errors.
  They work well for printed text in form-like documents, but unstructured,
  handwritten documents still remain a challenge. Adapting existing models to
  domain-specific training data is quite expensive, because of two factors, 1)
  limited availability of the domain-specific documents (such as handwritten
  prescriptions, lab notes, etc.), and 2) annotations become even more
  challenging as one needs domain-specific knowledge to decode inscrutable
  handwritten document images. In this work, we focus on the complex problem of
  extracting medicine names from handwritten prescriptions using only weakly
  labeled data. The data consists of images along with the list of medicine names
  in it, but not their location in the image. We solve the problem by first
  identifying the regions of interest, i.e., medicine lines from just weak labels
  and then injecting a domain-specific medicine language model learned using only
  synthetically generated data. Compared to off-the-shelf state-of-the-art
  methods, our approach performs >2.5x better in medicine names extraction from
  prescriptions.
paper_ai_keywords: OCR errors text extraction image-specific training handwriting recognition domain-specific documents domain-specific knowledge weakly labeled data regions of interest medicine language model synthetically generated data