How do I learn about optical word recognition in deep learning using Python without using conventional techniques?
Latest Deep Learning OCR with Keras and Supervisely in 15 minutes s Deep Learning based Text Recognition (OCR) using Tesseract and OpenCV s Deep Learning OCR using TensorFlow and Python s Deep Dive Into OCR for Receipt Recognition - DZone AI s Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning s Tutorial Alphabet Recognition In Real Time A Deep Learning and OpenCV Application s Image Text Recognition in Python Face recognition with OpenCV Python and deep learning - PyImageSearch s s s s+CVDL11+CVDL11_T1 s+CVDL11+CVDL11_T1 s s go through this all the best for your future
What mathematical background does one need for learning Deep Learning?
Any technical background especially engineering is sufficient. So if you are an engineer no problem. The level of maths background you need is actually simpler than you think non. You can get away by using DL as a black box really no special skills other than the ability to do basic API calls into ML libraries like TensorFlow. Anybody can write ML code with a few lines of code using Python and an appropriate ML library. So the question here is ambiguous in the sense that it doesn state the OP intended purpose is it to implement or design DL systems? For implementation anyone can do it as outlined above the minimum you need is a mere simple programming skill in any language preferably Python quick with a few lines of code you get things working especially with TensorFlow you can quickly write an MNIST OCR recognizer with about 99.2% accuracy with just a few lines of code not bad at all. For design of DL systems things get somewhatplicated and a lot of effort goes into this. The background in maths that is useful is not thatplicated though just basic linear algebra such as matrix manipulations statistics simple differential calculus and numeral optimization . These four are normally taught at advanced-levels (A-levels) before engineering courses they are simple really. Hope this helps.
What are the most popular Machine Learning Projects on Github?
As of June 3 217 by number of stars on Github (excluding tutorials and examples repositories) tensorflow s stars scikit-learn s stars fchollet s stars tesseract-ocr s stars dmlc s stars mxgmn s stars tflearn s stars clips s stars caffe2 s stars nltk s stars As you can see Tensorflow is in a league of its own when ites to popularity with stars far ahead second place scikit-learn stars. Keras is not too far behind at number 3 although with all the deep learning hype I wouldn be surprised if Keras gets second place soon. It interesting to note that all three have some sort of connection to Google. Tensorflow was developed at Google scikit-learn started as a Google Summer of Code project and Keras was developed by Francois Chollet a Googler. This observation reminded me of an xkcdic.
How can I make an OCR using python and machine learning libraries?
Unless you are doing it for learning don make one from scratch. Try using Tessaract tesseract-ocr s . It is very good and already has a Deep Learning based model ( tesseract-ocr s ) integrated which works on a lot of data. If your data is too different from how looks in real world try finetuning it (as given here tesseract-ocr s ) . If you are learning some good starting points are CRNN bgshih s and s s . Use PyTorch or Tensorflow to code them up.
When does it make sense to use TensorFlow instead of OpenCV for computer vision?
TF is a library that provides you with an interface to create neural network architectures in the form ofputational graphs. This does not necessarily meanputer vision. OpenCV is aputer vision focused library that allows you to do many things. Rememberputer vision does not necessarily involve machine learning let alone deep learning. Calculating HOG LBP or SIFT features or segmenting an and OpenCV when you want to doputer vision tasks that they provide interfaces to which are often the more classicalputer vision tasks. Use TF when you would like to solve aputer vision task using deep learning.
What is the difference beween OpenFace, OpenCV and OpenBR?
OpenCv.. OpenBR and OpenFace are all Computer vision frameworks they serve different purpose but they're all OpenSource libraries. OpenCv OpenCv is the most powerfulputer vision library among BR and Face. OpenCv is not only related to Image recognition it can be used to build other cool stuff related toputer vision. You can build a range of projects using Open CV ranging from applying a filter to your photos OCR detection from livestream Video frames etc. You can also build face recognition modules by training your own data sets. But OpenCv isn't fullypatible with Nueral networks. It contains more than 2 Algorithms. OpenFace OpenFace makes use of Deep Nueral networks to implement face recognition. OpenFace is basically a python implementation. Many pertained models are available for use. OpenFace is really simple to use. You can create a recognition. You can build your own recognition model on top of it rather than building from scratch. Training Nueral nets is aputationaly intensive task. So Google cloud offers you their TPUs to build a model on the cloud. Narasimha1997 s . I just built a project using inception 3 it can recognise almost all using my phone and get back the result. I'm planning to build a demo app which makes use of this server for image recognition. One of the main advantage of Building servers for AI is that.. it mainly reduces your application size you can concentrate on other features to add and your app can work faster. If you are planning to build a simple Ocr face tracking application you can go for Google mobile vision. Its really simple and lightweight. Follow this Mobile Vision | Google Developers s . Thanks for A2A . )
How can I implement OCR technique in apache spark?
There are a number of additional techniques that you may want to check out for applying OCR techniques with Apache Spark Utilize the Tesseract-OCR via PyTesseract (via PySpark) pytesseract .1.6 s While not OCR per se here an interesting -analysis-and-analytics-using-spark A fun one is Tim Hunter blog postbining Apache Spark and Google TensorFlow via TensorFrames Deep Learning with Apache Spark and TensorFlow s Another approach is to use a Data Science service such as Algorithmia with their various OCR algorithms ocr Algorithms - Algorithmia s . You can find a sample of how to make a call to Algorithmia from Spark at algorithmiaio s HTH!