OCR, or [[Optical Character Recognition]], is a technology that enables the conversion of images containing typed or printed text into machine-encoded text. It uses various algorithms and techniques to analyze the shapes and patterns of characters in an image and then converts them into editable and searchable text. OCR plays a crucial role in digitizing physical documents, such as scanned paper documents, invoices, receipts, or even images captured by cameras. By extracting the text from these documents, OCR allows for easier editing, searching, and manipulation of the content. It eliminates the need for manual data entry and significantly speeds up document processing. The process of OCR typically involves several steps: 1. Image preprocessing: This step involves enhancing the quality of the input image by adjusting brightness, contrast, removing noise or artifacts to improve OCR accuracy. 2. Text localization: The OCR algorithm identifies regions in the image that contain text by analyzing patterns and shapes. 3. Character segmentation: In this step, individual characters are separated from each other within the identified text regions. 4. Feature extraction: OCR algorithms extract specific features from each character to identify them accurately. These features can include stroke width, curvature, line thickness, or other relevant characteristics. 5. Character recognition: Using machine learning or pattern recognition techniques, OCR algorithms compare extracted features with pre-trained models to recognize each character and convert it into machine-readable text. 6. Post-processing: After character recognition, post-processing techniques are applied to refine the results further. These techniques may include error correction methods like spell-checking or grammar analysis. OCR technology has numerous applications across various industries and sectors. Some common uses include: 1. Document digitization: OCR is widely used to convert printed documents into editable electronic formats like Word documents or searchable PDFs. 2. Data extraction: OCR can automatically extract specific information from forms or invoices such as names, addresses, dates, or financial data for further processing. 3. Text-to-speech conversion: OCR technology is used to convert printed text into speech, enabling accessibility for visually impaired individuals. 4. Language translation: By converting printed text into machine-readable format, OCR can facilitate language translation processes. 5. Automated data entry: OCR allows for automating data entry tasks by extracting information from physical documents and entering it directly into databases or other computer systems. OCR technology continues to evolve with advancements in computer vision, artificial intelligence, and deep learning techniques. It has become increasingly accurate and efficient, opening up new possibilities for document management, automation, and information retrieval. # References ```dataview Table title as Title, authors as Authors where contains(subject, "OCR") or contains(subject, "tesseract.js") ```