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Learn Ocr Computer Vision Tutorial, validate concepts with Ocr Computer Vision MCQ Questions, and prepare interviews through Ocr Computer Vision Interview Questions and Answers.
Optical Character Recognition MCQ
Find text regions, normalize crops, and transcribe characters or sequences—printed or in the wild.
Text
Unicode
Detection
Boxes / masks
Recognition
Sequence
Scene
Wild text
Reading text in images
OCR splits into locating text (detection) and reading glyphs or sequences (recognition). Classical pipelines use segment-then-classify; deep models use CNN+RNN+CTC or attention decoders for line-level text. Scene text in photos is harder than scanned documents due to blur, perspective, and clutter.
Detection vs recognition
You can detect word/quadrilateral boxes with a detector, crop rectified patches, then run a sequence recognizer—end-to-end models combine both.
Key ideas
Text detection
EAST, DB, or segmentation masks for text regions.
Line recognition
Reshape feature maps to sequence; RNN + CTC or attention.
CTC loss
Aligns variable-length outputs without per-character frame alignment.
Lexicon / LM
Constrains decoding with dictionaries or language models.
Classic stack
detect → deskew / rectify → segment characters or line CRNN → post-process