Normalization and Diacritics: Understanding the Polish Character Conundrum

Introduction

In this article, we will delve into the world of Unicode normalization and explore how it can be used to handle diacritics in text data. Specifically, we’ll examine a common issue where certain characters, like the Polish letter “ł,” are not properly handled when converting text from non-ASCII encodings to ASCII.

Background

Unicode is a standard for representing text in computers using unique numerical codes. It includes a vast array of characters from all languages and scripts. However, this complexity also brings challenges when working with text data, particularly when dealing with diacritics – small marks that modify the meaning of letters.

Normalization is a process used to normalize Unicode code points to their base form, which can be thought of as the “core” or “plain” version of a character. There are four main normalization forms in Unicode:

NFD (Normalization Form Decomposition): breaks down characters into their base form and diacritic parts.
NFC (Normalization Form Composition): combines base forms with diacritics to create the original character.
NFKD (Normalization Form Compatibility Decomposition): similar to NFD but also handles compatibility characters.
NFKC (Normalization Form Compatibility Composition): similar to NFC but also handles compatibility characters.

In this article, we’ll focus on NFD and NFC, as they are the most commonly used normalization forms for text processing.

The Problem with Diacritics

Diacritics can be a source of confusion when working with text data. For example, in Polish, there are two letters that have no diacritic: “ć” and “ż.” However, when these characters are combined with other letters, they gain diacritics.

In the case of the question presented, we’re trying to convert text from non-ASCII encodings (e.g., UTF-16) to ASCII. However, as we’ll see in the next section, this conversion process can sometimes result in the loss of Polish characters, like “ł.”

Normalization Forms and Diacritics

Now that we understand the basics of normalization, let’s dive deeper into NFD and NFC.

NFD (Normalization Form Decomposition): This form breaks down characters into their base form (a letter or a digit) and diacritic parts. The base form is then used to create the normalized character.
For example, the Polish letter “ł” would be broken down into its base form (“l”) and diacritic part ( ).
```
# NFD (Normalization Form Decomposition)
'ł' -> 'l' + U+0304 (Combining Diacritical Mark)
```
NFC (Normalization Form Composition): This form combines the base forms with their diacritic parts to create the original character. The diacritic part is then ignored.
Using the same example as above, NFC would combine “l” and U+0304 to get the original “ł” character.
```
# NFC (Normalization Form Composition)
'l' + U+0304 -> 'ł'
```

Why `ignore` in Python’s `str.encode()` Method?

The question states that it thinks the issue with the Polish letter “ł” is due to the ignore parameter in Python’s str.encode() method. The encode() method converts a string to bytes, and the errors='ignore' parameter tells Python to ignore any invalid or undefined Unicode characters during this process.

When using NFD as part of the normalization pipeline, some characters (like “ł”) will be broken down into their base form and diacritic parts. The base form is then used for encoding to ASCII. However, when these characters are encountered in text data, Python’s str.encode() method ignores the diacritic part (U+0304), effectively removing it from the string.

The Solution: Using Unidecode

As suggested by the answer to the question on Stack Overflow, we can use a library called unidecode to handle this issue. The unidecode function converts Unicode characters to their closest ASCII equivalent without altering the original formatting or encoding of the text.

Here’s how you could implement this solution:

# Using Unidecode
from unidecode import unidecode

for column in df.columns:
    df[column] = [unidecode(x) for x in df[column].values]

In this code, we iterate over each column in the dataframe. For each value in that column, we use the unidecode() function to convert it to its closest ASCII equivalent.

Conclusion

Normalization is a powerful tool for handling diacritics in text data. However, as we’ve seen, it can also be a source of confusion and loss of characters when converting between different Unicode normalization forms.

In this article, we explored the basics of NFD and NFC, including how they handle diacritics and how Python’s str.encode() method interacts with these characters.

We also discovered that using unidecode can help solve issues like the one presented in the question, as it handles the conversion to ASCII without altering the original formatting or encoding of the text.

Last modified on 2023-06-01