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automatickeyphraseextraction

Data for Automatic Keyphrase Extraction Task

cascadetabnet

This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"

continuous_cnn

Author released code for "Continuous CNN For Nonuniform Time Series (ICASSP 21')"

crowdsourcing_datasets

cse231-llvm-project

Data flow analysis and optimization. This is the default course project of CSE231 in UCSD.

densenet-caffe

DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet

dreal4

SMT Solver for Nonlinear Theories of Reals

graph_cluster

hawkes

Python class for generation and parameter estimation of multivariate Hawkes processes

karel

Karel dataset for program synthesis and program induction

learn_cfg_with_neural_network

matlab-examples

Set of awesome Matlab Examples

mip

populus

portrait_matting

shihui2010.github.io

socialbuzz

star_trail

symbolic_simplifier

PyTorch implementation for the Deep Symbolic Simplification Without Human Knowledge

table-extraction-from-url

Web page PDF/PNG rendering done right. Self-hosted service for rendering receipts, invoices, or any content.

table-transformer

Model training and evaluation code for our dataset PubTables-1M, developed to support the task of table extraction from unstructured documents.

tablenet

TableNet: Deep Learning model for end-to-end Table Detection and Tabular data extraction from Scanned Data Images In modern times, more and more number of people are sharing their documents as photos taken from smartphones. A lot of these documents contain lots of information in one or more tables. These tables often contain very important information and extracting this information from the image is a task of utmost importance. In modern times, information extraction from these tables is done manually, which requires a lot of effort and time and hence is very inefficient. Therefore, having an end-to-end system that given only the document image, can recognize and localize the tabular region and also recognizing the table structure (columns) and then extract the textual information from the tabular region automatically will be of great help since it will make our work easier and much faster. TableNet is just that. It is an end-to-end deep learning model that can localize the tabular region in a document image, understand the table structure and extract text data from it given only the document image. Earlier state-of-the-art deep learning methods took the two problems, that is, table detection and table structure recognition (recognizing rows and columns in the table) as separate and treated them separately. However, given the interdependence of the two tasks, TableNet considers them as two related sub-problems and solves them using a single neural network. Thus, also making it relatively lightweight and less compute intensive solution.

tensorflow

Computation using data flow graphs for scalable machine learning

tutorials

PyTorch tutorials.

twitter-lda

Latent Dirichlet Allocation (LDA) model for Microblogs (Twitter, weibo etc.)

wikidt-eval

word_attraction

Word Attraction Model for Unsupervised Key Word Extraction

yolo_tensorflow

tensorflow implementation of 'YOLO : Real-Time Object Detection'

zju-icicles

浙江大学课程攻略共享计划