Why No-Code Annotation and OCR?
Many AI projects require data that is not ready for modeling yet. Text may need labels, images may need categories or bounding boxes, and scanned documents may need to be converted into editable text.
Module goal: learners practice two practical no-code workflows: using Label Studio to create labeled datasets and using Google Docs OCR to extract text from scanned PDFs or images.
This module connects directly to library and GLAM workflows, including digitized collections, metadata enrichment, image description, transcription review, and preparing training data for AI models.
The No-Code Workflow
The module follows a simple workflow that moves from raw material to AI-ready data.
1. Collect
Start with unlabeled text, images, PDFs, or document scans.
2. Import
Upload data into Label Studio or open scanned files with Google Docs.
3. Configure
Choose a labeling template such as text classification, image classification, OCR, or bounding boxes.
4. Annotate
Assign labels, mark entities, draw boxes, or correct extracted OCR text.
5. Review
Check whether labels are consistent and whether OCR output needs human correction.
6. Export
Export labeled data as CSV or JSON for later cleaning, analysis, or model training.
Task 1: Text Classification with Label Studio
Label Studio lets learners turn unlabeled text into a labeled dataset through a graphical interface. A common beginner task is sentiment analysis, where each row of text is labeled as positive, negative, neutral, or uncertain.
Demo steps
- Create a new Label Studio project.
- Import a CSV file with unlabeled text.
- Select a text classification template.
- Add or revise label options.
- Label each task and export the result.
Teaching point
The interface helps learners understand that supervised AI requires examples with both input data and human-assigned labels.
Task 2: Image Classification and Object Detection
Label Studio can also support computer vision tasks. Learners can classify whole images, such as identifying cats and dogs, or draw bounding boxes around objects such as cars, houses, signs, or collection items.
Why this matters: image labels can describe what an image contains, while bounding boxes describe where an object appears in the image. This difference is important for AI systems that work with visual collections.
- Image classification: choose one or more labels for an entire image.
- Object detection: draw a box around each target object and assign a label.
- Export: save annotations with image paths, labels, and bounding-box coordinates.
Task 3: OCR with Google Docs
OCR stands for Optical Character Recognition. In this workflow, learners use Google Drive and Google Docs to convert scanned PDFs or images with text into editable document text.
Demo steps
- Upload a PDF or image file to Google Drive.
- Right-click the file.
- Select Open with → Google Docs.
- Review the image at the top and extracted text below.
- Edit OCR mistakes before reuse.
Quality reminder
OCR works better when the scan or photo is clear, well-lit, straight, and high contrast. Human review is still necessary because OCR can misread words, punctuation, layout, or handwriting.
Task 4: OCR Annotation with Label Studio
For more structured OCR tasks, Label Studio can be used to mark regions of a document image and type the text that appears in each region. This is useful when learners want both the location of text and the transcribed content.
- Document image: upload a scan or photograph of a page.
- OCR template: choose the optical character recognition template under computer vision.
- Region marking: draw a box around a text area.
- Transcription: enter the text shown in that region.
This workflow is especially relevant for historical documents, forms, receipts, handwritten notes, and digitized local collections.
Core Skills in This Module
Annotation = Add Meaning
Learners practice turning raw data into usable AI examples by adding labels, categories, boxes, or transcriptions.
Templates = Task Design
Choosing the right template helps learners connect a dataset to a specific AI task such as classification, NER, object detection, or OCR.
OCR = Text Extraction
OCR converts visual text into editable text, but learners must review the output for accuracy and formatting errors.
Export = Reuse
Exported CSV or JSON files can be cleaned, analyzed, shared, or used as training data in later AI workflows.
Library and GLAM Applications
- Digitized collections: extract and correct text from scanned archival materials.
- Metadata support: label topics, object types, or document categories for later analysis.
- Image collections: classify images or mark objects in photographs and exhibits.
- Instruction: show learners how human judgment shapes AI-ready datasets.
- Local AI projects: prepare small, custom datasets for classification, retrieval, or chatbot experiments.
Video Tutorials
Replace each YouTube link placeholder with the final video URL after uploading.
Introduces Label Studio as an open-source tool for creating labeled datasets from unlabeled data, including text classification, image classification, object detection, OCR, and intent/slot labeling.
Demonstrates how to use Google Drive and Google Docs to convert scanned PDFs or images into editable text through optical character recognition.
Learning Outcomes
- Explain why labeled data is necessary for many supervised AI workflows.
- Create a Label Studio project and import text, image, or document data.
- Choose appropriate templates for text classification, image classification, object detection, and OCR tasks.
- Use Google Docs OCR to extract editable text from scanned PDFs or image files.
- Evaluate OCR quality and identify why human correction remains important.
- Export labeled or extracted data for later cleaning, analysis, or AI model development.