Finance Commons and the Bad Data Toolbox

Document AI toolbox ready-to-use and optimized for bad data

PremiumNewProductProductivityDocument AIOCR Correction

Finance Commons and the Bad Data Toolbox are a suite of models and tools for document AI research and applications. They focus on handling bad data, including OCR errors and chaotic text, to enhance the robustness of AI in document processing. These tools and models help to automate workflows, reduce the workload for businesses in preparing content, and support development of next-generation multi-modal document models.

\OCronos: OCR correction decoder model for correcting OCR errors.\
\Segmentext: Text segmentation encoding model to improve text structure.\
\Bibtexer: Structured literature information extraction encoding model.\
\PleIAs-Editor: An integrated workflow to make bad text suitable for advanced retrieval applications.\
\Reversed-Zotero: A tool that automatically converts unstructured bibliographic data into BibTex data.\

\The target audience for this product is primarily businesses and research institutions that need to handle large volumes of documents and data
particularly in fields such as finance
law
and government. These tools and models can help them increase the automation of document processing
reduce human intervention

\Financial institutions use Finance Commons models to automatically parse and understand annual financial statements.\
\Legal teams use the Segmentext model from the Bad Data Toolbox to handle complex legal documents.\
\Government agencies use the Reversed-Zotero tool to convert historical archives into BibTex data suitable for retrieval.\

\1. Visit the HuggingFace platform for Finance Commons and Bad Data Toolbox.\
\2. Select the appropriate model based on your need
such as OCR correction or text segmentation.\

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20899836

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Finance Commons and the Bad Data Toolbox Alternatives

ChatGPT Text Divider — Divides text into blocks of 3000 characters.

Productivity•Text Segmentation•Tool

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