Spaces:
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title: Form Understanding Using LayoutLMV3
emoji: 🐠
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 3.35.2
app_file: app.py
pinned: false
license: afl-3.0
The Form Understanding project focuses on utilizing the LayoutLMv3 model for extracting and understanding information from structured documents, specifically forms. The goal is to automatically process forms and extract relevant data from different sections, including questions, answers, headings, sub-headings, and other text elements.
The LayoutLMv3 model, a popular Multimodel architecture, is specifically designed for document layout understanding tasks. It takes into account the spatial information and visual characteristics of text elements within a document to enhance its understanding of the content.
In the project, the LayoutLMv3 model is trained and fine-tuned on a dataset of forms with labeled sections such as questions, answers, headings, sub-headings, and other relevant sections. This training enables the model to learn the patterns and features associated with each section type.
During the inference phase, the trained LayoutLMv3 model is applied to new forms. The model analyzes the layout and content of the form, identifies the different sections based on their visual cues, and extracts the relevant information from each section. This process involves detecting and classifying text elements into the predefined categories, such as questions, answers, headings, sub-headings, and other sections.
By automating the form understanding process using LayoutLMv3, the project aims to streamline data extraction from forms, improve accuracy, and reduce manual effort. The extracted information can be further processed and used for various applications, such as data entry, form classification, data analysis, and more. This project would the first step to automating legal, medical and governmental records.
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference