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With The Rapid Growth Of Digital Information Processing, Extracting And Understanding Text From Images Has Become An Essential Task In Various Domains Such As Document Digitization, Education, And Multilingual Communication. Optical Character Recognition (OCR) Technology Enables The Automatic Conversion Of Printed Or Handwritten Text In Images Into Machine-readable Formats. This Project Focuses On Implementing An OCR-based System Using The Tesseract Engine To Detect And Extract Text From Images, Followed By Automatic Translation Into The Desired Language. The Methodology Involves Preprocessing Input Images Through Techniques Such As Grayscale Conversion, Noise Removal, And Thresholding To Enhance Accuracy. Once Text Is Extracted Using Tesseract OCR, A Translation Module Is Applied To Convert The Recognized Text Into The Target Language, Enabling Cross-lingual Accessibility. The Proposed System Provides An Efficient And Scalable Solution For Bridging Language Barriers And Making Text-based Information More Accessible For Diverse Users. Applications Of This Work Include Real-time Translation For Travelers, Digitization Of Multilingual Documents, And Assistance For Individuals With Limited Language Proficiency.