Building a Fixed-Length CAPTCHA OCR Model With Multi-Head Classification
About this episode
The episode discusses the design of a fixed-length CAPTCHA OCR model using a multi-head CNN architecture.
This story was originally published on HackerNoon at: https://hackernoon.com/building-a-fixed-length-captcha-ocr-model-with-multi-head-classification . How a multi-head CNN with position embeddings achieved 100% accuracy on fixed-length CAPTCHA OCR without using CRNNs or CTC loss. Check more stories related to futurism at: https://hackernoon.com/c/futurism . 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. This story was written by: @genesys . Learn more about this writer by checking @genesys's about page, and for more stories, please visit hackernoon.com . 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
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