OCR for Indian languages has made significant progress, with many tools now supporting scripts like Devanagari, Bengali, Tamil, and Telugu. Solutions such as Google Tesseract and Microsoft Azure OCR offer robust support for printed text recognition in Indian languages. However, challenges remain in recognizing handwritten text and degraded documents, as the complexity of Indic scripts and lack of high-quality datasets limit accuracy. Ongoing research and the use of deep learning models are improving performance. Initiatives like Google’s Project Sandhan and specialized regional OCR systems are helping bridge the gap. While OCR for Indian languages is not yet perfect, it is steadily improving and becoming more accessible.
What is the Status of OCR in Indian languages?

- Information Retrieval 101
- GenAI Ecosystem
- AI & Machine Learning
- Vector Database 101: Everything You Need to Know
- Retrieval Augmented Generation (RAG) 101
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is the role of TensorFlow in NLP?
TensorFlow is a deep learning framework that plays a significant role in NLP by providing tools for building and trainin
Can Marble ai also generate navigable 3D worlds purely from text?
Marble ai can conceptually generate navigable 3D worlds from text only by treating text as a high-level description of s
How does disaster recovery address communication systems?
Disaster recovery plays a crucial role in ensuring that communication systems remain operational during and after a disa