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This Project Presents A Python-based Application That Converts Text Embedded In Images Into Editable, Translatable Text And Delivers Fluent Outputs In A Target Language. The System Couples Image Preprocessing (noise Removal, Binarization, Skew Correction) With Optical Character Recognition (OCR) To Extract Text From Varied Inputs Such As Documents, Signboards, And Screenshots. Language Identification Triggers A Neural Machine Translation Pipeline To Produce The Translated Text, While Confidence Scores Guide Optional Human Review. A Lightweight GUI Enables Drag-and-drop Images, Batch Processing, And Export To TXT/PDF. The Implementation Leverages Open Libraries For Computer Vision And OCR, Supports On-device Processing For Privacy, And Can Fall Back To Online Translation Services For Higher Quality. Experiments On Multilingual Datasets Evaluate OCR Accuracy, Translation Quality (BLEU/chrF), And Latency Across Device Profiles. Results Show That Careful Preprocessing And Model Selection Substantially Improve End-to-end Quality, Making The Tool Practical For Education, Travel, And Accessibility Use Cases (e.g., Assisting Low-vision Users). The System’s Modular Design Facilitates Future Upgrades, Including Domain-specific Glossaries And Fully Offline Neural Translation.