{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"Turning Non-Standard Business Documents Into Structured, Verifiable Data","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/97c578c3\"></iframe>","width":"100%","height":180,"duration":450,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/turning-non-standard-business-documents-into-structured-verifiable-data.\nOCR reads the words but doesn't guarantee correct data. How layout models, table detection, and verification turn messy business documents into trusted output.\nCheck more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #ai, #unstructured-data-processing, #unstructured-data, #llms, #ocr, #optical-character-recognition, #multimodal, #multimodal-pipeline,  and more.\nThis story was written by: @navsuresh. Learn more about this writer by checking @navsuresh's about page,\n            and for more stories, please visit hackernoon.com.\nBusiness documents don't follow templates, so template-based parsers fail on them. OCR reads the words but can still lose the layout that gives a number its meaning. Break the pipeline into stages so each failure type is testable, and attach a source and confidence score to every extracted value. Then send only the uncertain ones to a human.","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}