This is a repository of legal tech startup activities and projects.
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Updated
May 6, 2022
This is a repository of legal tech startup activities and projects.
dataset containing a list of past leads in the sales funnel of an IT Services company.
Benchmarking dataset of degraded word images (with character splits) in Kannada along with their associated ground truth Unicode text
Service to import data from various sources and index it in AI Search. Increases data relevance and reduces final size by 90%+. Useful for RAG scenarios with LLM. Hosted in Azure with serverless architecture.
Get the number of columns for a document image
Document understanding with GPT 3.5 integrated with Telegram
official repo for the ICDAR 2023 paper "Line Graphics Digitization: A Step Towards Full Automation"
PdfDet aims to simplify PDF layout detect tasks for users.
This project lets you analyze a document in two ways: Question Generator: It automatically creates various questions from your document. Memory-Based Chatbot: Ask the chatbot questions about the document, and it will answer using its knowledge of the content.
Python code to perform keyword spotting using SIFT features
A fast and accurate command line tool for extracting text from PDF files.
The objective of this repository is to provide MuRET's users a simple way to train deep learning models allowing an efficient transcription process.
This is the repository for the backend, AI models and API development of AymurAI
A Discord bot powered by Google Gemini Pro, capable of text generation, image analysis, audio transcription, and more.
Muisca: Modelo Unificado de Inteligencia Supervisada para la Computación y Aplicación. Una herramienta Streamlit para resumir y hacer preguntas sobre documentos en PDF y TXT utilizando modelos de lenguaje de última generación.
📄 Create an opinionated analysis report of a document, influenced by personality traits.
Code for the paper "Gaussian Process Classification as Metric Learning for Forensic Writer Identification", published at DAS 2018
Chatbot that answers questions about user-uploaded documents
All the material (paper, code, dataset, results) of our DAS 2022 paper (OCR+NER benchmark)
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