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.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence