What is OCR Transcription?
OCR Transcription is the process of using Optical Character Recognition (OCR) technology to convert text found in images, scanned documents, or handwritten notes into machine-readable and editable digital text.
While OCR extracts characters, transcription ensures that the content is correctly interpreted, structured, and ready for use—whether for search, editing, analysis, or archiving.
How OCR Transcription Works
The process typically follows these steps:
1. Image Acquisition
- Scanned documents, photos, or camera-captured images of text are collected.
- Common sources: Printed books, invoices, ID cards, handwritten forms, historical manuscripts.
2. Preprocessing
- Enhances image quality to improve accuracy.
- Noise reduction
- Binarization (black-and-white conversion)
- Deskewing (aligning tilted documents)
- Contrast adjustment
- Cropping and segmentation
3. Text Detection & Recognition
- Text regions are detected using computer vision algorithms.
- Each region is processed using:
- Template matching
- Machine learning models (e.g., CNNs)
- Deep learning-based OCR (like Tesseract, Google Vision AI, or PaddleOCR)
4. Transcription
- Extracted text is structured into readable format:
- Lines, paragraphs, tables, columns, or forms.
- Manual or semi-automated corrections may be applied for:
- Misrecognized characters
- Formatting errors (e.g., misplaced columns)
- Complex layouts (e.g., forms with mixed fonts)
5. Post-processing
- Spell check and grammar correction
- Language models help detect and fix common OCR errors.
- Metadata tagging (like headers, footers, page numbers)