Optical Character Recognition (OCR) for Indian languages has seen steady progress but remains challenging due to linguistic diversity and technical limitations. Most efforts focus on major languages like Hindi, Bengali, Tamil, Telugu, and Gujarati, which use scripts such as Devanagari, Bengali, Tamil, and others. Open-source tools like Tesseract OCR have added support for some Indian scripts, but accuracy varies significantly compared to Latin-based languages. Commercial solutions like Google’s Document AI and Amazon Textract offer better performance for certain scripts but lack comprehensive coverage. For example, Hindi OCR works reasonably well for printed text, but languages with complex ligatures (e.g., Urdu Nastaliq) or fewer digital resources (e.g., Manipuri Meitei) lag behind. Academic projects, such as those from IITs, have developed custom models for specific languages, but these are often not production-ready.
Key challenges include script complexity, limited datasets, and font variations. Indian scripts often involve conjunct characters (e.g., Devanagari’s “क्ष” or Tamil’s “க் + ஷ”), which require specialized segmentation and recognition logic. Handwritten text recognition is particularly underdeveloped due to the lack of large, annotated datasets. Font diversity also complicates OCR—many regional languages use non-standard fonts that aren’t adequately represented in training data. For instance, older printed materials in Malayalam or Kannada often use legacy fonts that confuse modern OCR systems. Additionally, dialectal variations in scripts, like the differences between Bengali used in India and Bangladesh, create edge cases that generic models struggle to handle.
Future progress depends on improved datasets and localized model architectures. Initiatives like the Indian government’s Bhashini project aim to crowdsource datasets for NLP and OCR tasks. Researchers are experimenting with hybrid models combining convolutional neural networks (CNNs) for feature extraction and transformers for context-aware decoding. Tools like EasyOCR have started integrating support for Indian languages by fine-tuning existing models on smaller, domain-specific datasets. For example, efforts to digitize historical Sanskrit manuscripts use custom-trained models that account for archaic glyphs. Developers working on Indian OCR should prioritize script-specific preprocessing (e.g., reordering vowel signs in Gurmukhi) and collaborate with linguists to address structural nuances. While gaps persist, focused efforts in data collection and model optimization could narrow the accuracy gap with Latin-script OCR in the next 3–5 years.