feat: add Kombi-Modus (PaddleOCR + Tesseract) for OCR Overlay
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Runs both OCR engines on the preprocessed image and merges results: word boxes matched by IoU, coordinates averaged by confidence weight. Unmatched Tesseract words (bullets, symbols) are added for better coverage. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -11,12 +11,12 @@ import { StepRowDetection } from '@/components/ocr-pipeline/StepRowDetection'
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import { StepWordRecognition } from '@/components/ocr-pipeline/StepWordRecognition'
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import { OverlayReconstruction } from '@/components/ocr-overlay/OverlayReconstruction'
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import { PaddleDirectStep } from '@/components/ocr-overlay/PaddleDirectStep'
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import { OVERLAY_PIPELINE_STEPS, PADDLE_DIRECT_STEPS, DOCUMENT_CATEGORIES, dbStepToOverlayUi, type PipelineStep, type SessionListItem, type DocumentCategory } from './types'
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import { OVERLAY_PIPELINE_STEPS, PADDLE_DIRECT_STEPS, KOMBI_STEPS, DOCUMENT_CATEGORIES, dbStepToOverlayUi, type PipelineStep, type SessionListItem, type DocumentCategory } from './types'
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const KLAUSUR_API = '/klausur-api'
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export default function OcrOverlayPage() {
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const [mode, setMode] = useState<'pipeline' | 'paddle-direct'>('pipeline')
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const [mode, setMode] = useState<'pipeline' | 'paddle-direct' | 'kombi'>('pipeline')
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const [currentStep, setCurrentStep] = useState(0)
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const [sessionId, setSessionId] = useState<string | null>(null)
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const [sessionName, setSessionName] = useState<string>('')
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@@ -63,13 +63,17 @@ export default function OcrOverlayPage() {
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setSessionName(data.name || data.filename || '')
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setActiveCategory(data.document_category || undefined)
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// Check if this session was processed with paddle_direct
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const isPaddleDirect = data.word_result?.ocr_engine === 'paddle_direct'
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// Check if this session was processed with paddle_direct or kombi
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const ocrEngine = data.word_result?.ocr_engine
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const isPaddleDirect = ocrEngine === 'paddle_direct'
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const isKombi = ocrEngine === 'kombi'
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if (isPaddleDirect) {
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setMode('paddle-direct')
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if (isPaddleDirect || isKombi) {
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const m = isKombi ? 'kombi' : 'paddle-direct'
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const baseSteps = isKombi ? KOMBI_STEPS : PADDLE_DIRECT_STEPS
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setMode(m)
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setSteps(
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PADDLE_DIRECT_STEPS.map((s, i) => ({
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baseSteps.map((s, i) => ({
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...s,
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status: i < 4 ? 'completed' : i === 4 ? 'active' : 'pending',
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})),
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@@ -101,7 +105,7 @@ export default function OcrOverlayPage() {
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if (sessionId === sid) {
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setSessionId(null)
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setCurrentStep(0)
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const baseSteps = mode === 'paddle-direct' ? PADDLE_DIRECT_STEPS : OVERLAY_PIPELINE_STEPS
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const baseSteps = mode === 'kombi' ? KOMBI_STEPS : mode === 'paddle-direct' ? PADDLE_DIRECT_STEPS : OVERLAY_PIPELINE_STEPS
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setSteps(baseSteps.map((s, i) => ({ ...s, status: i === 0 ? 'active' : 'pending' })))
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}
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} catch (e) {
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@@ -158,7 +162,7 @@ export default function OcrOverlayPage() {
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const handleNext = () => {
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if (currentStep >= steps.length - 1) {
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// Last step completed — return to session list
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const baseSteps = mode === 'paddle-direct' ? PADDLE_DIRECT_STEPS : OVERLAY_PIPELINE_STEPS
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const baseSteps = mode === 'kombi' ? KOMBI_STEPS : mode === 'paddle-direct' ? PADDLE_DIRECT_STEPS : OVERLAY_PIPELINE_STEPS
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setSteps(baseSteps.map((s, i) => ({ ...s, status: i === 0 ? 'active' : 'pending' })))
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setCurrentStep(0)
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setSessionId(null)
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@@ -187,7 +191,7 @@ export default function OcrOverlayPage() {
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setSessionId(null)
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setSessionName('')
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setCurrentStep(0)
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const baseSteps = mode === 'paddle-direct' ? PADDLE_DIRECT_STEPS : OVERLAY_PIPELINE_STEPS
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const baseSteps = mode === 'kombi' ? KOMBI_STEPS : mode === 'paddle-direct' ? PADDLE_DIRECT_STEPS : OVERLAY_PIPELINE_STEPS
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setSteps(baseSteps.map((s, i) => ({ ...s, status: i === 0 ? 'active' : 'pending' })))
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}
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@@ -226,7 +230,7 @@ export default function OcrOverlayPage() {
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}, [sessionId, goToStep])
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const renderStep = () => {
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if (mode === 'paddle-direct') {
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if (mode === 'paddle-direct' || mode === 'kombi') {
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switch (currentStep) {
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case 0:
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return <StepOrientation sessionId={sessionId} onNext={handleOrientationComplete} />
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@@ -237,7 +241,21 @@ export default function OcrOverlayPage() {
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case 3:
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return <StepCrop sessionId={sessionId} onNext={handleNext} />
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case 4:
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return <PaddleDirectStep sessionId={sessionId} onNext={handleNext} />
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return mode === 'kombi' ? (
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<PaddleDirectStep
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sessionId={sessionId}
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onNext={handleNext}
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endpoint="paddle-kombi"
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title="Kombi-Modus"
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description="PaddleOCR und Tesseract laufen parallel. Koordinaten werden gewichtet gemittelt fuer optimale Positionierung."
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icon="🔀"
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buttonLabel="Paddle + Tesseract starten"
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runningLabel="Paddle + Tesseract laufen..."
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engineKey="kombi"
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/>
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) : (
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<PaddleDirectStep sessionId={sessionId} onNext={handleNext} />
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)
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default:
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return null
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}
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@@ -480,13 +498,29 @@ export default function OcrOverlayPage() {
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>
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Paddle Direct (5 Schritte)
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</button>
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<button
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onClick={() => {
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if (mode === 'kombi') return
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setMode('kombi')
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setCurrentStep(0)
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setSessionId(null)
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setSteps(KOMBI_STEPS.map((s, i) => ({ ...s, status: i === 0 ? 'active' : 'pending' })))
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}}
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className={`px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${
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mode === 'kombi'
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? 'bg-white dark:bg-gray-700 text-gray-700 dark:text-gray-200 shadow-sm'
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: 'text-gray-500 dark:text-gray-400 hover:text-gray-700 dark:hover:text-gray-300'
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}`}
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>
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Kombi (5 Schritte)
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</button>
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</div>
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<PipelineStepper
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steps={steps}
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currentStep={currentStep}
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onStepClick={handleStepClick}
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onReprocess={mode === 'pipeline' && sessionId ? reprocessFromStep : undefined}
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onReprocess={mode === 'pipeline' && sessionId != null ? reprocessFromStep : undefined}
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/>
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<div className="min-h-[400px]">{renderStep()}</div>
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@@ -60,6 +60,18 @@ export const PADDLE_DIRECT_STEPS: PipelineStep[] = [
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{ id: 'paddle-direct', name: 'PaddleOCR + Overlay', icon: '⚡', status: 'pending' },
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]
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/**
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* 5-step pipeline for Kombi mode (PaddleOCR + Tesseract).
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* Same preprocessing, then both engines run and results are merged.
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*/
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export const KOMBI_STEPS: PipelineStep[] = [
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{ id: 'orientation', name: 'Orientierung', icon: '🔄', status: 'pending' },
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{ id: 'deskew', name: 'Begradigung', icon: '📐', status: 'pending' },
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{ id: 'dewarp', name: 'Entzerrung', icon: '🔧', status: 'pending' },
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{ id: 'crop', name: 'Zuschneiden', icon: '✂️', status: 'pending' },
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{ id: 'kombi', name: 'Paddle + Tesseract', icon: '🔀', status: 'pending' },
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]
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/** Map from DB step to overlay UI step index */
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export function dbStepToOverlayUi(dbStep: number): number {
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// DB: 1=start, 2=orient, 3=deskew, 4=dewarp, 5=crop, 6=columns, 7=rows, 8=words, 9=recon, 10=gt
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@@ -10,14 +10,38 @@ type Phase = 'idle' | 'running' | 'overlay'
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interface PaddleDirectStepProps {
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sessionId: string | null
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onNext: () => void
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/** Backend endpoint suffix, default: 'paddle-direct' */
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endpoint?: string
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/** Title shown in idle state */
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title?: string
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/** Description shown in idle state */
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description?: string
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/** Icon shown in idle state */
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icon?: string
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/** Button label */
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buttonLabel?: string
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/** Running label */
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runningLabel?: string
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/** OCR engine key to check for auto-detect */
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engineKey?: string
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}
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export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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export function PaddleDirectStep({
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sessionId,
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onNext,
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endpoint = 'paddle-direct',
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title = 'Paddle Direct',
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description = 'PaddleOCR erkennt alle Woerter direkt auf dem Originalbild — ohne Begradigung, Entzerrung oder Zuschnitt.',
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icon = '⚡',
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buttonLabel = 'PaddleOCR starten',
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runningLabel = 'PaddleOCR laeuft...',
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engineKey = 'paddle_direct',
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}: PaddleDirectStepProps) {
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const [phase, setPhase] = useState<Phase>('idle')
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const [error, setError] = useState<string | null>(null)
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const [stats, setStats] = useState<{ cells: number; rows: number; duration: number } | null>(null)
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// Auto-detect: if session already has paddle_direct word_result → show overlay
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// Auto-detect: if session already has matching word_result → show overlay
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useEffect(() => {
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if (!sessionId) return
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let cancelled = false
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@@ -26,7 +50,7 @@ export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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const res = await fetch(`${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}`)
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if (!res.ok || cancelled) return
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const data = await res.json()
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if (data.word_result?.ocr_engine === 'paddle_direct') {
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if (data.word_result?.ocr_engine === engineKey) {
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setPhase('overlay')
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}
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} catch {
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@@ -34,14 +58,14 @@ export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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}
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})()
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return () => { cancelled = true }
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}, [sessionId])
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}, [sessionId, engineKey])
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const runPaddleDirect = useCallback(async () => {
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const runOcr = useCallback(async () => {
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if (!sessionId) return
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setPhase('running')
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setError(null)
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try {
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const res = await fetch(`${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/paddle-direct`, {
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const res = await fetch(`${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/${endpoint}`, {
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method: 'POST',
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})
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if (!res.ok) {
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@@ -59,7 +83,7 @@ export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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setError(e instanceof Error ? e.message : 'Unbekannter Fehler')
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setPhase('idle')
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}
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}, [sessionId])
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}, [sessionId, endpoint])
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if (!sessionId) {
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return (
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@@ -91,7 +115,7 @@ export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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<div className="w-10 h-10 border-4 border-teal-200 dark:border-teal-800 border-t-teal-600 dark:border-t-teal-400 rounded-full animate-spin" />
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<div className="text-center space-y-1">
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<p className="text-sm font-medium text-gray-700 dark:text-gray-300">
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PaddleOCR laeuft...
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{runningLabel}
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</p>
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<p className="text-xs text-gray-400">
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Bild wird analysiert (ca. 5-30s)
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@@ -101,12 +125,12 @@ export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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) : (
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<>
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<div className="text-center space-y-2">
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<div className="text-4xl">⚡</div>
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<div className="text-4xl">{icon}</div>
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<h3 className="text-lg font-medium text-gray-700 dark:text-gray-300">
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Paddle Direct
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{title}
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</h3>
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<p className="text-sm text-gray-500 dark:text-gray-400 max-w-md">
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PaddleOCR erkennt alle Woerter direkt auf dem Originalbild — ohne Begradigung, Entzerrung oder Zuschnitt.
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{description}
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</p>
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</div>
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@@ -117,10 +141,10 @@ export function PaddleDirectStep({ sessionId, onNext }: PaddleDirectStepProps) {
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)}
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<button
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onClick={runPaddleDirect}
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onClick={runOcr}
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className="px-6 py-2.5 bg-teal-600 text-white text-sm font-medium rounded-lg hover:bg-teal-700 transition-colors"
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>
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PaddleOCR starten
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{buttonLabel}
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</button>
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</>
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)}
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@@ -2599,6 +2599,189 @@ async def paddle_direct(session_id: str):
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return {"session_id": session_id, **word_result}
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def _box_iou(a: dict, b: dict) -> float:
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"""Compute IoU between two word boxes (each has left, top, width, height)."""
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ax1, ay1 = a["left"], a["top"]
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ax2, ay2 = ax1 + a["width"], ay1 + a["height"]
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bx1, by1 = b["left"], b["top"]
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bx2, by2 = bx1 + b["width"], by1 + b["height"]
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ix1, iy1 = max(ax1, bx1), max(ay1, by1)
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ix2, iy2 = min(ax2, bx2), min(ay2, by2)
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inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)
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if inter == 0:
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return 0.0
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area_a = (ax2 - ax1) * (ay2 - ay1)
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area_b = (bx2 - bx1) * (by2 - by1)
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return inter / (area_a + area_b - inter) if (area_a + area_b - inter) > 0 else 0.0
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def _merge_paddle_tesseract(paddle_words: list, tess_words: list) -> list:
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"""Merge word boxes from PaddleOCR and Tesseract.
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Matching: IoU > 0.3 between bounding boxes.
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Merging: Weighted average of coordinates by confidence.
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"""
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merged = []
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used_tess: set = set()
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for pw in paddle_words:
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best_iou, best_ti = 0.0, -1
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for ti, tw in enumerate(tess_words):
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if ti in used_tess:
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continue
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iou = _box_iou(pw, tw)
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if iou > best_iou:
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best_iou, best_ti = iou, ti
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if best_iou > 0.3 and best_ti >= 0:
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tw = tess_words[best_ti]
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used_tess.add(best_ti)
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pc = pw.get("conf", 80)
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tc = tw.get("conf", 50)
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total = pc + tc
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if total == 0:
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total = 1
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merged.append({
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"text": pw["text"], # Paddle text usually better
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"left": round((pw["left"] * pc + tw["left"] * tc) / total),
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"top": round((pw["top"] * pc + tw["top"] * tc) / total),
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"width": round((pw["width"] * pc + tw["width"] * tc) / total),
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"height": round((pw["height"] * pc + tw["height"] * tc) / total),
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"conf": max(pc, tc),
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})
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else:
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merged.append(pw)
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# Add unmatched Tesseract words (bullet points, symbols, etc.)
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for ti, tw in enumerate(tess_words):
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if ti not in used_tess and tw.get("conf", 0) >= 40:
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merged.append(tw)
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return merged
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@router.post("/sessions/{session_id}/paddle-kombi")
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async def paddle_kombi(session_id: str):
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"""Run PaddleOCR + Tesseract on the preprocessed image and merge results.
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Both engines run on the same preprocessed (cropped/dewarped) image.
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Word boxes are matched by IoU and coordinates are averaged weighted by
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confidence. Unmatched Tesseract words (bullets, symbols) are added.
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"""
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img_png = await get_session_image(session_id, "cropped")
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if not img_png:
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img_png = await get_session_image(session_id, "dewarped")
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if not img_png:
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img_png = await get_session_image(session_id, "original")
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if not img_png:
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raise HTTPException(status_code=404, detail="No image found for this session")
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img_arr = np.frombuffer(img_png, dtype=np.uint8)
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img_bgr = cv2.imdecode(img_arr, cv2.IMREAD_COLOR)
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if img_bgr is None:
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raise HTTPException(status_code=400, detail="Failed to decode image")
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img_h, img_w = img_bgr.shape[:2]
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from cv_ocr_engines import ocr_region_paddle
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t0 = time.time()
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# --- PaddleOCR ---
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paddle_words = await ocr_region_paddle(img_bgr, region=None)
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if not paddle_words:
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paddle_words = []
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# --- Tesseract ---
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from PIL import Image
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import pytesseract
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pil_img = Image.fromarray(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB))
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data = pytesseract.image_to_data(
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pil_img, lang="eng+deu",
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config="--psm 6 --oem 3",
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output_type=pytesseract.Output.DICT,
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)
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tess_words = []
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for i in range(len(data["text"])):
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text = str(data["text"][i]).strip()
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conf_raw = str(data["conf"][i])
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conf = int(conf_raw) if conf_raw.lstrip("-").isdigit() else -1
|
||||
if not text or conf < 20:
|
||||
continue
|
||||
tess_words.append({
|
||||
"text": text,
|
||||
"left": data["left"][i],
|
||||
"top": data["top"][i],
|
||||
"width": data["width"][i],
|
||||
"height": data["height"][i],
|
||||
"conf": conf,
|
||||
})
|
||||
|
||||
# --- Merge ---
|
||||
if not paddle_words and not tess_words:
|
||||
raise HTTPException(status_code=400, detail="Both OCR engines returned no words")
|
||||
|
||||
merged_words = _merge_paddle_tesseract(paddle_words, tess_words)
|
||||
|
||||
cells, columns_meta = build_grid_from_words(merged_words, img_w, img_h)
|
||||
duration = time.time() - t0
|
||||
|
||||
for cell in cells:
|
||||
cell["ocr_engine"] = "kombi"
|
||||
|
||||
n_rows = len(set(c["row_index"] for c in cells)) if cells else 0
|
||||
n_cols = len(columns_meta)
|
||||
col_types = {c.get("type") for c in columns_meta}
|
||||
is_vocab = bool(col_types & {"column_en", "column_de"})
|
||||
|
||||
word_result = {
|
||||
"cells": cells,
|
||||
"grid_shape": {"rows": n_rows, "cols": n_cols, "total_cells": len(cells)},
|
||||
"columns_used": columns_meta,
|
||||
"layout": "vocab" if is_vocab else "generic",
|
||||
"image_width": img_w,
|
||||
"image_height": img_h,
|
||||
"duration_seconds": round(duration, 2),
|
||||
"ocr_engine": "kombi",
|
||||
"grid_method": "kombi",
|
||||
"summary": {
|
||||
"total_cells": len(cells),
|
||||
"non_empty_cells": sum(1 for c in cells if c.get("text")),
|
||||
"low_confidence": sum(1 for c in cells if 0 < c.get("confidence", 0) < 50),
|
||||
"paddle_words": len(paddle_words),
|
||||
"tesseract_words": len(tess_words),
|
||||
"merged_words": len(merged_words),
|
||||
},
|
||||
}
|
||||
|
||||
await update_session_db(
|
||||
session_id,
|
||||
word_result=word_result,
|
||||
cropped_png=img_png,
|
||||
current_step=8,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"paddle_kombi session %s: %d cells (%d rows, %d cols) in %.2fs "
|
||||
"[paddle=%d, tess=%d, merged=%d]",
|
||||
session_id, len(cells), n_rows, n_cols, duration,
|
||||
len(paddle_words), len(tess_words), len(merged_words),
|
||||
)
|
||||
|
||||
await _append_pipeline_log(session_id, "paddle_kombi", {
|
||||
"total_cells": len(cells),
|
||||
"non_empty_cells": word_result["summary"]["non_empty_cells"],
|
||||
"paddle_words": len(paddle_words),
|
||||
"tesseract_words": len(tess_words),
|
||||
"merged_words": len(merged_words),
|
||||
"ocr_engine": "kombi",
|
||||
}, duration_ms=int(duration * 1000))
|
||||
|
||||
return {"session_id": session_id, **word_result}
|
||||
|
||||
|
||||
class WordGroundTruthRequest(BaseModel):
|
||||
is_correct: bool
|
||||
corrected_entries: Optional[List[Dict[str, Any]]] = None
|
||||
|
||||
Reference in New Issue
Block a user