L = -1/N ∑ [y_i log(ŷ_i) + (1 - y_i) log(1 - ŷ_i)]Attention(Q, K, V) = softmax(QK^T / √d_k)Vf(x) = 1 / (1 + e^{-x})∇ × E = -∂B/∂tH(p, q) = -∑ p(x) log q(x)E[X] = ∫ x f(x) dxP(A|B) = P(B|A)P(A) / P(B)w^T x + b = 0θ_{t+1} = θ_t - η ∇_θ J(θ)
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v2.0.4 // ONLINE
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NLP & Vision // 2025-05

Document OCR Engine

Demo: Left Panel ←
Source Code

End-to-end text extraction from noisy documents, receipts, and ID cards with layout preservation. Combines a lightweight CRNN for text recognition with a LayoutLMv3 model for understanding document structure and key-value pair extraction.

Role

Machine Learning Engineer

Technologies

PaddleOCRTransformersLayoutLM

Use Cases & Advantages

- Automated Invoice and Receipt Processing - Digital Archiving of Legacy Paper Documents - KYC Document Data Extraction - Healthcare Record Digitization Our Stack's Advantage: Seamlessly pairs LayoutLMv3 for structural understanding with high-speed inference on CPU instances, drastically reducing cloud operational costs.