OCR & Document Understanding
OCR is a three-stage pipeline — detect text boxes, recognise the characters, then lay them out. Every modern OCR system reorders these stages or merges them. Trace the classical OCR pipeline (detect -> recognise -> layout) and the modern end-to-end alternatives (Donut, Qwen-VL-OCR). Implement CTC (Connectionist Temporal Classification) loss for sequence-to-sequence OCR training. Use PaddleOCR or EasyOCR for production document parsing without training. Distinguish OCR, layout parsing, and document understanding — and pick the right tool per task. Images full of text are everywhere: receipts, invoices, IDs, scanned books, forms, whiteboards, signs, screenshots. Extracting structured data from them — not just the characters, but "this is the total amount" — is one of the highest-value applied-vision problems. The field splits into three skill layers: OCR proper: turn pixels into text. Layout parsing: group OCR output into regions (title, body, table, header). Document understanding: extract structured fields ("invoicetotal = $42.50") from layout. Each layer has classical and modern approaches, and the gap between "I want text from an image" and "I need the total amount from this receipt" is bigger than most teams realise. Text detection produces per-line or per-word quadrilaterals. Recognition crops each region to a fixed height, runs a CNN + BiLSTM + CTC to produce a character sequence. Layout rebuilds reading order (top-to-bottom, left-to-right for Latin; different for…
OCR & Document Understanding: OCR is a three-stage pipeline — detect text boxes, recognise the characters, then lay them out. Every modern OCR system reorders…
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