OCR and document understanding
Understand how machines read scans, forms and tables, and how to check that they read correctly
A taste of a lesson
I used an AI model to read 500 receipts. The text looks clean. Do I still need to check it?
Yes, and clean looking text is exactly why. Multimodal models tend to produce fluent output even where the image is blurry, so a smudged 8 can quietly become a 3 with no warning. Start with automatic checks: do line items add up to the total, do dates parse, are amounts in a plausible range? Then hand check a random sample of around 30 receipts field by field against the images and count errors per field. That tells you which fields need review every time. Quick exercise: name one validation rule that would catch a misread total.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe the stages of an OCR pipeline and what can fail at each one
- Improve results with sensible scanning and preprocessing choices
- Measure OCR quality with character, word and field level accuracy
- Judge when a vision language model is suitable for reading documents
- Design an extraction workflow with validation, review and privacy safeguards
Lesson plan
- 1 From image to text: the pipeline Name the stages of OCR and see where errors enter on a real document. Start
- 2 Scan quality and preprocessing Get better input images and clean them before recognition. Start
- 3 Layout, tables and handwriting Understand why structure and handwriting are much harder than plain printed text. Start
- 4 Measuring reading quality Compute error rates and choose metrics that reflect what matters for your task. Start
- 5 Vision language models as document readers Use multimodal models for documents while guarding against invented text. Start
- 6 A trustworthy extraction workflow Combine extraction, validation rules, human review and privacy handling. Start
Try asking
About this tutor
For beginners who need to get text and data out of scanned documents, photos of receipts, forms or PDFs, and want to know what is happening behind the tools. You will learn the classic pipeline of image clean up, text detection, text recognition and layout analysis, then see how tables and handwriting make each stage harder. The course explains character and word error rates so you can measure quality, and looks honestly at vision language models that read documents end to end, including their habit of producing plausible text that is not on the page. You finish by designing a small extraction workflow with validation rules, human review and sensible handling of private data.
Reviews
4.3
3 ratingsSample
- Bongani T.Sample
The totals check idea caught 14 misread receipts in my first batch. I had assumed the output was fine because it looked so tidy.
- Tariq S.Sample
Practical and calm. I would have liked more on tables specifically, since that is most of my work, but the reading order explanation helped.
- Anna V.Sample
Good explanation of CER and why field accuracy matters more. The handwriting lesson was honest that my old family letters will need a lot of manual work.
About the teacher
Computer vision taught through real images, real failure cases and careful evaluation
9 tutors 406 lessons taught Sample
I teach computer vision: classification, detection, segmentation, document understanding, video and the newer models that combine images with language. Most of my work has been building vision systems that had to hold up outside the lab, under odd lighting, unusual cameras and labels that were not quite consistent. So my lessons spend as much time on data and evaluation as...
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