Multilingual Handwritten OCR using CLIP and Tesseract

Optical Character Recognition (OCR) of handwritten text is an extremely challenging problem, particularly in multilingual and low-resource environments. Conventional OCR engines like Tesseract work well for printed text but not for handwriting because of extreme variations in style, language, and noise. The breakthroughs in multimodal models, especially CLIP (Contrastive Language–Image Pretraining), provide new avenues agnostic knowledge This paper discusses the possibility of combining CLIP with Tesseract to improve multilingual handwritten OCR, covering current methods, limitations, and future research directions.