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Clean OCR data and build an LLM dataset | Microsoft Interview Question

Q: Clean OCR data and build an LLM dataset

This question evaluates competency in OCR data cleaning and normalization, dataset engineering for LLM fine-tuning, quality filtering, and evaluation design for text-correction tasks.

Problem: OCR data practice (cleaning → LLM-ready data)

You are given an OCR dataset intended to train or fine-tune an LLM to improve OCR text quality.

Input

A dataset of records like:

image_id (or image path)
ocr_text (raw OCR output)
Optional: ground_truth_text (human-labeled), language , source , timestamps, confidence scores, bounding boxes, etc.

Tasks

Data cleaning & normalization
- Propose a cleaning pipeline to prepare high-quality text pairs for training.
- Handle common OCR artifacts (broken Unicode, random whitespace/newlines, hyphenation at line breaks, repeated headers/footers, page numbers, garbage tokens).
- Define rules/heuristics and what you would log/measure.
Filtering & quality control
- Identify and remove low-quality or risky samples (PII, toxic content, extremely noisy OCR, duplicates, near-duplicates, misaligned labels if ground_truth_text exists).
- If no ground truth exists, explain how you would estimate quality.
Create an LLM training/eval split
- Prevent leakage (e.g., same document across train/test).
- Propose evaluation sets and metrics for OCR-correction.
Modeling approach
- Explain how you would train an LLM (or instruction-tune) for OCR correction given the cleaned dataset.
- Specify input/output format (prompting style), loss/objective, and any baselines.
Deliverables
- Describe what artifacts you would produce (cleaned dataset schema, reports, dashboards, model cards, etc.).

Problem: OCR data practice (cleaning → LLM-ready data)

You are given an OCR dataset intended to train or fine-tune an LLM to improve OCR text quality.

Input

A dataset of records like:

image_id (or image path)

ocr_text (raw OCR output)

Optional: ground_truth_text (human-labeled), language , source , timestamps, confidence scores, bounding boxes, etc.

Tasks

Data cleaning & normalization

Propose a cleaning pipeline to prepare high-quality text pairs for training.
Handle common OCR artifacts (broken Unicode, random whitespace/newlines, hyphenation at line breaks, repeated headers/footers, page numbers, garbage tokens).
Define rules/heuristics and what you would log/measure.

Filtering & quality control

Identify and remove low-quality or risky samples (PII, toxic content, extremely noisy OCR, duplicates, near-duplicates, misaligned labels if ground_truth_text exists).
If no ground truth exists, explain how you would estimate quality.

Create an LLM training/eval split

Prevent leakage (e.g., same document across train/test).
Propose evaluation sets and metrics for OCR-correction.

Modeling approach

Explain how you would train an LLM (or instruction-tune) for OCR correction given the cleaned dataset.
Specify input/output format (prompting style), loss/objective, and any baselines.

Deliverables

Describe what artifacts you would produce (cleaned dataset schema, reports, dashboards, model cards, etc.).

Clean OCR data and build an LLM dataset

Quick Overview

Clean OCR data and build an LLM dataset

Problem: OCR data practice (cleaning → LLM-ready data)

Input

Tasks

Write your answer

Clean OCR data and build an LLM dataset

Quick Overview

Clean OCR data and build an LLM dataset

Problem: OCR data practice (cleaning → LLM-ready data)

Input

Tasks

Write your answer