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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

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

Input

Tasks

Solution

Comments (0)

Clean OCR data and build an LLM dataset

Quick Overview

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

Input

Tasks

Solution

Comments (0)