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Building a Fixed-Length CAPTCHA OCR Model With Multi-Head Classification
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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By HackerNoonThis 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