Multi-Type-TD-TSR
transformers-interpret
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| Multi-Type-TD-TSR | transformers-interpret | |
|---|---|---|
| 4 | 3 | |
| 236 | 1,207 | |
| - | - | |
| 0.0 | 2.9 | |
| over 1 year ago | 8 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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Multi-Type-TD-TSR
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[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)
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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)
transformers-interpret
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[P] XAI Recipes for the HuggingFace 🤗 Image Classification Models
Very cool, I like seeing this. I also noticed the transformers interpret package has released support for an image classification explainer: https://github.com/cdpierse/transformers-interpret
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Using LIME to explain the predictions from a BERT model, it looks like "the", "and", "or" are "very important" features, and thus I don't think the model is learning anything interesting. Any tips?
You could look at the Transformers Interpret python library: https://github.com/cdpierse/transformers-interpret
- Show HN: Transformers Interpret – Explain and visualize Transformer models
What are some alternatives?
donut - Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
neuro-symbolic-sudoku-solver - ⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
MetalTranslate - Customizable machine translation in C++
small-text - Active Learning for Text Classification in Python
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.
gensim - Topic Modelling for Humans
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"
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
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.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
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.
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]