---
title: Optical Character Recognition for Educational Assessment
type: templates
hide_menu: true
category: Computer Vision
cat: computer-vision
order: 1103
meta_description: Template for using Label Studio to perform optical character recognition (OCR).
---
Optical Character Recognition (OCR) labeled data is vital for AI applications in educational assessment, enabling models to accurately extract and interpret written text from scanned documents. This capability facilitates tasks such as scoring exams, analyzing student submissions, and automating administrative processes, which can ultimately enhance the quality and efficiency of educational evaluation.
However, the data labeling process for OCR presents significant challenges: it is often time-intensive due to the volume of documents that require attention, can suffer from inconsistency due to varied handwriting or text formatting, and necessitates domain expertise to ensure accurate interpretation of educational content. Label Studio effectively addresses these challenges through its hybrid AI-assisted pre-labeling, which accelerates initial labeling efforts while maintaining high accuracy rates. The platform's expert validation feature ensures that labels meet the required standards, empowering teams to collaborate seamlessly with customizable templates tailored specifically for educational assessment tasks. This approach not only streamlines the labeling process but also enhances model performance and scalability, allowing organizations to process large volumes of data efficiently and effectively.
Open in Label Studio
## Labeling configuration
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This labeling configuration lets you perform educational assessment annotation tasks by identifying regions on the image with shapes and transcribing the corresponding question or answer text. You can select the label type (e.g., "Multiple Choice") and then highlight relevant text regions to associate transcriptions with each region.
All labeling configurations must be wrapped in View tags.
Use the Image object tag to specify the image to label:
```xml
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Use the Labels control tag to specify which labels are available to apply to the different shapes added to the image:
```xml
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You can change the value of each Label to assign different assessment item types to regions in the educational content image.
Use the Rectangle control tag to add unlabeled rectangles:
```xml
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Choosing the Rectangle tag instead of RectangleLabels means annotators can first create regions highlighting questions or answers, then assign labels, then transcribe the text for each region. This workflow facilitates stepwise annotation in educational assessment.
Use the Polygon control tag to add unlabeled polygons:
```xml
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The strokeWidth argument controls the thickness of the polygon outline.
Use the TextArea control tag to add transcriptions for each region drawn on the image:
```xml
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The editable="true" setting allows annotators to revise transcription after submitting, and displayMode="region-list" shows the text boxes in the region list linked to each shape, easing updates. The perRegion="true" attribute means each transcription belongs to a specific region, and required="true" enforces transcription input before submissions. The placeholder text guides annotators before editing.
Related tags:
- Image
- Labels
- Rectangle
- Polygon
- TextArea