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?
- Natural Language Processing (NLP) Basics
- How to Pick the Right Vector Database for Your Use Case
- Embedding 101
- Exploring Vector Database Use Cases
- Getting Started with Zilliz Cloud
- 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
How does the concept of the “curse of dimensionality” influence the design of indexing techniques for vector search?
The curse of dimensionality—the challenge of analyzing data in high-dimensional spaces—forces indexing techniques for ve
How do Vision-Language Models handle contradictory or misleading text associated with an image?
Vision-Language Models (VLMs) are designed to connect visual information from images with textual descriptions. When fac
What is AutoML's role in natural language processing?
AutoML, or Automated Machine Learning, plays a significant role in natural language processing (NLP) by simplifying the