AI로 손으로 쓴 가족 요리법을 디지털화하는 방법

Traditional OCR (optical character recognition) was built for printed text with regular letter shapes. Handwriting is harder. Letters join, differ from one writer to the next, and fade on old cards. Tools built for this job are called handwritten text recognition (HTR). Transkribus, for example, is widely used for historical documents. Modern multimodal assistants such as ChatGPT, Gemini and Claude can also read handwriting from a photo. They use context to work out messy words, so they can tell that a scrawl after '2 cups' is probably 'flour'. That same strength causes the biggest misconception: that the AI's transcription is exact. When a word is hard to read, a model may fill in a likely guess without saying so. In recipes, small differences matter. Reading teaspoon as tablespoon for salt or baking soda can ruin a dish. The fix is to do two passes and keep both outputs. First, ask for a verbatim transcription with unclear words marked. Second, ask for a standardized version with consistent units, numbered steps and modern oven temperatures, with every change explained. Old recipes use terms that need interpreting. 'Oleo' meant margarine. A 'moderate oven' is roughly 350°F (about 180°C). 'Scant' means a little less than full and 'heaping' means a little more. 'Butter the size of an egg' is an approximation, often read as about a quarter cup. Label these as interpretations, not facts. Good photos make everything else easier. Use soft daylight rather than flash, which causes glare on glossy cards. Hold the phone parallel to the card, fill the frame, and photograph the back, where notes often are. Keep the originals out of direct light, ideally in acid-free sleeves. The handwriting, the stains and the margin notes are part of the family record, and no transcription replaces them.