What is Khmer OCR and How Has It Progressed in the Khmer Language?

2 min readMay 11, 2025

What is OCR and How Has It Progressed in the Khmer Language?

I. What is OCR?

OCR (Optical Character Recognition) is a technology that converts text from scanned documents or images into editable, searchable digital text. It plays a key role in digitization — transforming physical or image-based documents into electronic formats like PDF, Image, Word, or searchable databases.

II. Why is OCR Important for the Khmer Language?

Khmer script is non-Latin, complex, and abugida-based, with stacked glyphs, sub-consonants, diacritics, and no spaces between words. This makes OCR development much harder than in English or other Latin-based languages. OCR for Khmer is crucial for:

Digitizing government forms, educational content, historical manuscripts
Improving accessibility and machine translation
Building AI tools in language understanding, search, and summarization for Khmer

III. Progress of OCR for Khmer Language

1. 📚 Traditional Methods (Before 2018)

OCR models used template matching, edge detection, and handcrafted features.
Poor generalization to new fonts, degraded images, and hand-written scripts.

2. 🤖 AI and Deep Learning Era (2018–2023)

Shift toward CNN + RNN (LSTM/GRU) architectures.
Attention-based models significantly improved printed Khmer OCR.
benchmarks and datasets:
Khmer OCR Dataset: includes printed and scanned Khmer documents.
Sleuk Rith OCR Dataset: historical palm-leaf manuscripts and Buddhist texts.
KhmerST: A synthetic dataset of street texts and signs in Khmer.

3. 🧠 Transformer-based Khmer OCR (2023–2025)

TrOCR, Donut (OCR-free), and mT5 fine-tuning on Khmer datasets show strong improvements in both OCR and post-correction tasks.
Research now focuses on:
Document Visual Question Answering (DocVQA) in Khmer
Language model-based error correction
OCR for mobile document capture (real-world conditions)
Pretrained language models like KhmerBERT, mBERT, and mT5 are used for post-OCR cleanup and classification.

IV. Challenges and Opportunities

🔻 Challenges:

Very few large-scale annotated datasets (especially for handwriting)
Low-resource nature: Khmer is not prioritized in major OCR tools
Font inconsistencies across scanned documents
Lack of public benchmarks for comparing models

🔺 Opportunities:

National digitization initiatives (e.g., e-Government, digital education) are increasing demand
Khmer OCR can support:
Archiving ancient documents and inscriptions
AI-powered search engines and document understanding
Voice interfaces that rely on text digitization
Growing interest in multilingual LLM alignment encourages the inclusion of under-resourced scripts like Khmer

🔚 Conclusion (2025 Perspective)

Khmer OCR has made significant progress over the past five years thanks to deep learning, synthetic data, and open-source tools. Yet the journey is far from over. The next breakthroughs will come from:

Larger and cleaner Khmer datasets
Cross-lingual pretraining
Integration into multimodal systems (image + text + layout)

With collaboration from academic institutions, startups, and the open-source community, Khmer OCR will become a powerful tool for Cambodia’s digital future.