DIF VS Multi-Type-TD-TSR

Compare DIF vs Multi-Type-TD-TSR and see what are their differences.

DIF

"DNA IMAGE FOOTPRINT" The main idea is to convert a DNA sequence to an image to find any related sequences in the image with common algorithms (by MahdiKarimian)
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DIF Multi-Type-TD-TSR
2 4
1 173
- -
1.5 2.8
about 1 year ago 3 months ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.

DIF

Posts with mentions or reviews of DIF. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning DIF yet.
Tracking mentions began in Dec 2020.

Multi-Type-TD-TSR

Posts with mentions or reviews of Multi-Type-TD-TSR. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-23.
  • [D] Getting super-level table extraction
    3 projects | reddit.com/r/MachineLearning | 23 Aug 2022
    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.

What are some alternatives?

When comparing DIF and Multi-Type-TD-TSR you can also consider the following projects:

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.

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.

MetalTranslate - Customizable machine translation in C++

donut - Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022