ocrserver
CloudForest
| ocrserver | CloudForest | |
|---|---|---|
| 2 | 4 | |
| 631 | 735 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | about 2 years ago | |
| Go | Go | |
| MIT License | GNU General Public License v3.0 or later |
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.
ocrserver
-
How to write a specific text recognition code?
For example, in about 5 minutes I was able to find ocrserver, hosted here. Drop your image into that and whitelist it to just numbers and you get the following output: { "result": "3\n7 2\n2 5\n120\n4\n12092\n42093 1 4\n12094\n7\n224\n2\n5 3\n25", "version": "0.2.0" }
-
https://np.reddit.com/r/AskProgramming/comments/nztt2k/how_to_write_a_specific_text_recognition_code/h1t0lkx/
For example, in about 5 minutes I was able to find ocrserver, hosted here. Drop your image into that and whitelist it to just numbers and you get the following output:
CloudForest
-
Trinary Decision Trees for missing value handling
I implemented something like this in a [pre xgboost boosting framework](https://github.com/ryanbressler/CloudForest) ~10 years ago and it worked well.
It isn't even that much of a speed hit using the classical sorting CART implementation. However xgboost and ligthgbm use histogram based approximate sorting which might be harder to adapt in a performant way. And certainly the code will be a lot messier.
-
Future of Golang
Personally, my Go-to ML tool for tabular data is here: https://github.com/ryanbressler/CloudForest
-
[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
What are some alternatives?
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
libsvm - libsvm go version
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
gobrain - Neural Networks written in go
tfgo - Tensorflow + Go, the gopher way
go-galib - Genetic Algorithms library written in Go / golang
goRecommend - Collaborative Filtering (CF) Algorithms in Go!
shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
goga - Golang Genetic Algorithm