{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Futurism Tech Brief By HackerNoon","title":"Building a Fixed-Length CAPTCHA OCR Model With Multi-Head Classification","html":"","width":"100%","height":180,"duration":986,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/building-a-fixed-length-captcha-ocr-model-with-multi-head-classification.\n How a multi-head CNN with position embeddings achieved 100% accuracy on fixed-length CAPTCHA OCR without using CRNNs or CTC loss. \n Check more stories related to futurism at: https://hackernoon.com/c/futurism.\n You can also check exclusive content about #computer-vision, #captcha-ocr, #crnn, #ctc-loss, #ocr-architecture, #multi-head-classification, #position-embeddings, #deep-learning, and more.\n \n \n This story was written by: @genesys. Learn more about this writer by checking @genesys's about page,\n and for more stories, please visit hackernoon.com.\n \n \n \n This article documents the design of a lightweight OCR system built to solve fixed-length numeric CAPTCHAs for authorized internal automation workflows. Instead of using a standard CRNN + CTC architecture, the author built a shared CNN backbone with six independent classification heads and learnable position embeddings, achieving 100% held-out accuracy with roughly 4,000 training samples while improving training stability, inference speed, and debuggability\n \n ","thumbnail_url":"https://img.transistorcdn.com/dkSY09WMT3S7SiI_n-P5daFmTJplJgc8AfjEgyM1Kqg/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcwLzE2ODM1/ODI1MTQtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}