AI OCR Text Cleanup Assistant Project

Artificial Intelligence project 17

AI OCR Text Cleanup Assistant Project

Last updated: 4 September 2026

Build a complete ocr text cleanup assistant workflow with reproducible Python code, transparent inputs, reviewable output, and responsible-use limits.

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How the system works

Tokenise while preserving punctuation, replace common OCR confusions in word-like tokens, use conservative dictionary similarity, and reassemble text.

Validation checklist

• Numbers without letters are preserved
• Corrections come only from the visible vocabulary
• Punctuation and spacing remain in place
• Manual comparison with the source image is required

Complete Python code

Save the program as ai_ocr_text_cleanup_assistant.py. The code runs locally and requires no paid API key or model download.

"""Clean common OCR character errors with an auditable vocabulary."""

from __future__ import annotations

from difflib import get_close_matches
import re


VOCABULARY = {"artificial", "intelligence", "project", "document", "learning", "model", "training", "data", "analysis", "python", "quality", "review", "invoice", "amount", "date", "total"}


def normalize_ocr_token(token: str) -> str:
    if not re.search(r"[A-Za-z]", token):
        return token
    candidate = token.lower().replace("0", "o").replace("1", "l").replace("|", "l")
    candidate = candidate.replace("rn", "m") if candidate not in VOCABULARY else candidate
    if candidate in VOCABULARY:
        return candidate
    matches = get_close_matches(candidate, VOCABULARY, n=1, cutoff=0.78)
    return matches[0] if matches else candidate


def clean_text(text: str) -> str:
    pieces = re.findall(r"[A-Za-z0-9|]+|[^A-Za-z0-9|]+", text)
    return "".join(normalize_ocr_token(piece) if re.fullmatch(r"[A-Za-z0-9|]+", piece) else piece for piece in pieces)


def main() -> None:
    noisy = "Artificia1 inte11igence rnodel training docurnent qua1ity review."
    print("Input: ", noisy)
    print("Cleaned:", clean_text(noisy))
    print("Review every correction against the source image; OCR cleanup can change meaning.")


if __name__ == "__main__":
    main()

Run the project

1. Create and activate a virtual environment.
2. Install dependencies with No third-party packages required when packages are required.
3. Run python ai_ocr_text_cleanup_assistant.py.
4. Review confidence, fallbacks, sources, or error metrics rather than accepting output automatically.
5. Test additional normal, edge, unsupported, and adversarial inputs.

Expected output

Cleaned text suitable for review alongside the original OCR result.

Ways to extend the project

Add confidence per edit, domain dictionaries, layout-aware OCR, amount reconciliation, language detection, and side-by-side approval.

Continue learning Artificial Intelligence

Try the next project, return to the Softenant project library, or explore the AI training in Vizag for guided NLP, retrieval, evaluation, automation, and responsible AI practice.