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Identity Verification Is A Critical Requirement In Sectors Such As Banking, Government Services, Security, And Digital Onboarding. Manual Extraction Of Information From Identity Cards Is Time-consuming, Error-prone, And Inefficient. To Address This Challenge, Optical Character Recognition (OCR) Integrated With Image Processing Techniques Offers An Automated And Reliable Solution. This Project Proposes The Development Of An Identity Card Recognition System That Captures And Processes Images Of ID Cards To Extract Textual Information Such As Name, Date Of Birth, Address, And Identification Number. The Methodology Involves Preprocessing The Image Using Techniques Like Grayscale Conversion, Noise Reduction, Edge Detection, And Contrast Enhancement To Improve Recognition Accuracy. The Processed Image Is Then Fed Into An OCR Engine, Such As Tesseract, To Convert Printed Or Handwritten Text Into A Machine-readable Format. Post-processing Steps, Including Text Segmentation And Error Correction, Further Refine The Extracted Data. The System Is Designed To Provide Fast, Accurate, And Secure Extraction Of Identity Information, Enabling Seamless Integration With Authentication And Verification Systems. This Approach Enhances Efficiency In Applications Like KYC (Know Your Customer), E-governance, And Digital Record Management While Reducing Manual Effort And Minimizing Fraud Risks.