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?

- How to Pick the Right Vector Database for Your Use Case
- Vector Database 101: Everything You Need to Know
- The Definitive Guide to Building RAG Apps with LangChain
- GenAI Ecosystem
- Accelerated Vector Search
- 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 are observability challenges in distributed databases?
Observability in distributed databases refers to the ability to monitor, understand, and troubleshoot system performance
How does benchmarking evaluate database reliability?
Benchmarking evaluates database reliability by systematically measuring how well a database performs under various condi
How does mcp maintain consistency across model-tool interactions?
MCP maintains consistency across model-tool interactions by enforcing structured schemas, predictable message formats, a