What are some of the overall most used classifiers in the machine learning community? Make an ordered list and describe the pros and cons for each one.
There are many possible classifiers used but here are some of them:

### Naive Bayes Classification Algorithm
#### Advantages
- It is simple, and its implementation is straightforward.
- The time required by the machine to learn the pattern using this classifier is less.
- It performs well in the case where the input variables have categorical values.
- It gives good results for complex real-world problems.
- It performs well in the case of multi-class classification.

#### Disadvantages
- It assumes independence among feature variables which may not always be the case.
- We often refer to it as a bad estimator, and hence the probabilities are not always of great significance.
- If, during the training time, the model was not aware of any of the categorical variables and that variable is passed during testing, the model assigns 0 (zero) likelihood and thus substitutes zero probability referred to as 'zero frequency.' One can avoid this situation by using smoothing procedures such as Laplace estimation.

### Logistic Regression Classification Algorithm
#### Advantages
- It's a simple model, so it takes very little time for training.
- It can handle a large number of features.

#### Disadvantages
- Although it has the word regression in its name, we can only use it for classification problems because of its range which always lies between 0 and 1.
- It can only be used for binary classification problems and has a poor response for multi-class classification problems

### Decision Tree Classification Algorithm
#### Advantages
- This algorithm allows for an uncomplicated representation of data. So, it is easier to interpret and explain it to executives.
- Decision Trees mimic the way humans make decisions in everyday life.
- They smoothly handle qualitative target variables.
- They handle non-linear data effectively.

#### Disadvantages
- They may create complex trees which sometimes become irrelevant.
- They do not have the same level of prediction accuracy as compared to other algorithms.

### K-Nearest Neighbour Classification Algorithm
#### Advantages
- One can apply it to datasets of any distribution.
- It is easy to understand and is quite intuitive.

#### Disadvantages
- It is easily affected by outliers.
- It is biased towards a class that has more instances in the dataset.
- It is sometimes challenging to find the optimal number for K.
Which one would be best for writing a basic OCR?
For writing a basic OCR, the most commonly used classifier is the K-Nearest Neighbor (KNN) algorithm. KNN works by assigning a class to an object by looking at the k-nearest neighbors in the feature space. In the case of OCR, the features of the image (such as pixel values) would be used to determine the nearest neighbors.

KNN is a good choice for OCR because it is simple and easy to implement, and can be used with both numeric and non-numeric data. However, it does have some limitations, such as being sensitive to outliers and being biased towards the majority class in the dataset.

It's important to note that OCR is a complex task that requires more than just a single classifier. Typically, OCR systems involve multiple components such as image preprocessing, feature extraction, and post-processing to improve the accuracy of the results.