What are the algorithms (ideally in pseudo-code) to convert a Unicode string into the normalised forms?
Unicode® Standard Annex #15 UNICODE NORMALIZATION FORMS does not give a concrete algorithm which can be directly coded in a language of choice.
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I'm asking for a way to convert multiple instances of LaTeX format, which is a typesetting language used for mathematical and technical documents, in a Word 2019 document to equations all at once.I mean the process of taking the text written in LaTeX code and converting it into a visual representation of an equation. In other words, the LaTeX format, which is a typesetting language primarily used for mathematical and technical documents, is being transformed into an image or visual representation of the equation that the code represents. It's like when you write an equation in word document, it is in text format but when you convert it to an equation, it will be a visual representation of the same equation.
For exmaple: The derivative of the natural logarithm function, $ln(x)$, with respect to $x$ is equal to the reciprocal of $x$, or mathematically written as: $$\frac{d}{dx} \ln(x) = \frac{1}{x}$$. In this text I have to convert each LaTeX code one by one to an equation such as $ln(x)$ then $x$ then $$\frac{d}{dx} \ln(x) = \frac{1}{x}$$.
In other words, I wants to find a way to quickly and efficiently convert all the LaTeX format in their Word document to equations, rather than having to do it one by one.
I read
Hashing is the transformation of arbitrary size input in the form of a fixed-size value. We use hashing algorithms to perform hashing operations i.e to generate the hash value of an input
And vector embeddings pretty much do the same that they convert an input into a vector of fixed dimension. Trying to understand the difference between them.
Hash encoding can use any function which can convert any string to a random unique number but while creating vector embeddings we try to use domain knowledge and context in which the string might have occurred in the corpus.
Binary relevance is a well known technique to deal with multi-label classification problems, in which we train a binary classifier for each possible value of a feature:
http://link.springer.com/article/10.1007%2Fs10994-011-5256-5
On the other side, one hot encoders (OHE) are commonly used in natural language processing to encode a categorical feature taking multiple values as a binary vector:
http://cs224d.stanford.edu/lecture_notes/LectureNotes1.pdf
Can we consider that these two concepts are the same one? Or are there technical differences?
Both methods are different.
1. One-Hot encoding
In one-hot encoding, vector is considered.
Above diagram represents binary classification problem.
2. Binary Relevance
In binary relevance, we do not consider vector. Following diagram represents class label generation using binary relevance method which is using scalar value.
A geohash is a convenient way of expressing a location (anywhere in the world) using a short alphanumeric string, with greater precision obtained with longer strings.
When I learn it the first time I was confused by its name. It's totally different to the other hashing algorithm, it keeps the information of the location. It's actually not a hashing algorithm but an encoding algorithm.
So how the algorithm named? Why it called Geohash?
Comments
To see the different between Encoding and Hashing you can click here: Encoding vs. Encryption vs. Hashing vs. Obfuscation
To see the Geohash algorithm in Java you can click here: Geohash Encoding and Decoding Algorithm
I am working on handwritten character recognition using neural networks. Currently I have segmented each character from the image. Now I want to extract features of each character so that I can feed them to the neural network. So what features should I extract from each character image?. Please suggest any sample code or procedure.
I am not sure if you should extract features from the characters. All the classic an novel methods feed the ANN using a downsampled version of the binary image that contains the character.
One example (1993) is this. There they use a 8x8 pixel version of the character as the ANN input. The question that #rafael-monteiro suggest in the comments also states that.
If your input images are big, maybe you want to try this method, or if you desire to extract some features, this work proposes some features, for exampl the angle of rotation of the parts of the character with more points or the aspect ratio of the character.