# Post-Processing in Text Extraction

Post-processing can recognizably improve the results of text extraction.
It is, however, outside of the scope of pypdf itself. Hence the library will
not give any direct support for it. It is a natural language processing (NLP)
task.

This page lists a few examples what can be done as well as a community
recipie that can be used as a best-practice general purpose post processing
step. If you know more about the specific domain of your documents, e.g. the
language, it is likely that you can find custom solutions that work better in
your context

## Ligature Replacement

```python
def replace_ligatures(text: str) -> str:
    ligatures = {
        "ﬀ": "ff",
        "ﬁ": "fi",
        "ﬂ": "fl",
        "ﬃ": "ffi",
        "ﬄ": "ffl",
        "ﬅ": "ft",
        "ﬆ": "st",
        # "Ꜳ": "AA",
        # "Æ": "AE",
        "ꜳ": "aa",
    }
    for search, replace in ligatures.items():
        text = text.replace(search, replace)
    return text
```

## De-Hyphenation

Hyphens are used to break words up so that the appearance of the page is nicer.

```python
from typing import List


def remove_hyphens(text: str) -> str:
    """

    This fails for:
    * Natural dashes: well-known, self-replication, use-cases, non-semantic,
                      Post-processing, Window-wise, viewpoint-dependent
    * Trailing math operands: 2 - 4
    * Names: Lopez-Ferreras, VGG-19, CIFAR-100
    """
    lines = [line.rstrip() for line in text.split("\n")]

    # Find dashes
    line_numbers = []
    for line_no, line in enumerate(lines[:-1]):
        if line.endswith("-"):
            line_numbers.append(line_no)

    # Replace
    for line_no in line_numbers:
        lines = dehyphenate(lines, line_no)

    return "\n".join(lines)


def dehyphenate(lines: List[str], line_no: int) -> List[str]:
    next_line = lines[line_no + 1]
    word_suffix = next_line.split(" ")[0]

    lines[line_no] = lines[line_no][:-1] + word_suffix
    lines[line_no + 1] = lines[line_no + 1][len(word_suffix) :]
    return lines
```

## Header/Footer Removal

The following header/footer removal has several drawbacks:

* False-positives, e.g. for the first page when there is a date like 2021.
* False-negatives in many cases:
    * Dynamic part, e.g. page label is in the header
    * Even/odd pages have different headers
    * Some pages, e.g. the first one or chapter pages, don't have a header

```python
def remove_footer(extracted_texts: list[str], page_labels: list[str]):
    def remove_page_labels(extracted_texts, page_labels):
        processed = []
        for text, label in zip(extracted_texts, page_labels):
            text_left = text.lstrip()
            if text_left.startswith(label):
                text = text_left[len(label) :]

            text_right = text.rstrip()
            if text_right.endswith(label):
                text = text_right[: -len(label)]

            processed.append(text)
        return processed

    extracted_texts = remove_page_labels(extracted_texts, page_labels)
    return extracted_texts
```

## Other ideas

* Whitespaces between Units: Between a number and it's unit should be a space
  ([source](https://tex.stackexchange.com/questions/20962/should-i-put-a-space-between-a-number-and-its-unit)).
  That means: 42 ms, 42 GHz, 42 GB.
* Percent: English style guides prescribe writing the percent sign following the number without any space between (e.g. 50%).
* Whitespaces before dots: Should typically be removed
* Whitespaces after dots: Should typically be added
