Multi-Type-TD-TSR
CodeSearchNet
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| Multi-Type-TD-TSR | CodeSearchNet | |
|---|---|---|
| 4 | 2 | |
| 236 | 1,904 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | about 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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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)
CodeSearchNet
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Fine tuning
The CodeSearchNet challenge provides a dataset of code documentation comments, along with pre-trained models and fine-tuning scripts. You can find the challenge and resources at https://github.com/github/CodeSearchNet.
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Speedtyper.dev: Type racing for programmers
https://github.com/github/CodeSearchNet#downloading-data-from-s3
What are some alternatives?
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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"
pycaret - An open-source, low-code machine learning library in Python
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.
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
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.
trulens - Evaluation and Tracking for LLM Experiments