Sentiment-Analysis
Multi-Type-TD-TSR
| Sentiment-Analysis | Multi-Type-TD-TSR | |
|---|---|---|
| 1 | 4 | |
| 1 | 236 | |
| - | - | |
| 10.0 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Sentiment-Analysis
-
Uncovering the Sentiment of British Airways Customer Feedback: A Data Science Project
GITHUB LINK TO THE PROJECT
Multi-Type-TD-TSR
-
[D] Getting super-level table extraction
Recently, I've been researching extracting tables from image documents. First I tried with pdfs, however, the data extraction libraries like camelot are inconsistent. I found a deep learning model called CascadeTabNet. The detection results are okay but cell recognition is poor. I even found Multi-Type-TD-TSR for table extraction. It uses image processing techniques to find the grids. It performs well on structured and bordered tables. However, it messes up if the cell is not properly aligned. Even if extraction is successful, aggregation of multi-line cells, i.e post-processing, is not very obvious.
- Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition (State of the art approach for table structure recognition published on KI2021 - 44th German Conference on Artificial Intelligence)
-
Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations
Check it out on my Github: https://github.com/Psarpei/Multi-Type-TD-TSR
- Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations (New state-of-the-art approach for table structure recognition)
What are some alternatives?
Advertisement-Recommendation - Video Content-Based Advertisement Recommendation Using Text Classification
donut - Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
SeeAI - Enabling computers to perform NLP on data obtained from advanced computer vision
MetalTranslate - Customizable machine translation in C++
Recognition-of-logical-document-structures - First approach for recognizing logical document structures like texts, sentences, segments, words, chars and sentence/segment depth based on recurrent neural network grammars.
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"
oemer - End-to-end Optical Music Recognition (OMR) system. Transcribe phone-taken music sheet image into MusicXML, which can be edited and converted to MIDI.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
ITC - Computer Science coursework and projects at Tec de Monterrey 👨🎓
elastic_transformers - Making BERT stretchy. Semantic Elasticsearch with Sentence Transformers
deathcounter_ocr - A python script which detects death messages by using OCR and displays a corrosponding deathcounter. Preconfigured for Elden Ring
docutron - Docutron Toolkit: detection and segmentation analysis for legal data extraction over documents.