How OCR Applications Work

OCR works by capturing an image and then analyzing the text within it. OCR analyzes the image character by character and recognizes letter or number patterns based on training data, including variations in font, size, and writing style. The results of this character analysis are then combined and converted into digital text data.

OCR Workflow

To make it easier to understand how OCR works, in simple terms OCR is divided into 4 workflow stages, which is: 

1. Document Input: Physical data that will be used must first be converted into an image. This can be done by photographing the document, scanning it, or providing a PDF file.
2. Pre-processing: To support character analysis, the captured image must be cleaned first. This includes straightening tilted documents, removing noise from photos, and adjusting the contrast between the background and the text so it is easier to read and analyze.
3. Processing: In this stage, the OCR application detects which parts of the image contain text and then analyzes them character by character based on the patterns, fonts, sizes, and writing styles stored in its training database.
4. Post-processing: At this stage, OCR results can be refined, for example through simple spell correction, formatting, and structural adjustments so the data is easier to edit, search, and compare with other data.

The Human Role in OCR Applications

Human involvement is not only about providing the technology, but more like a parent who must supervise and teach their child. 

1. Providing Training Data: OCR results are greatly influenced by the training data provided, and humans are the ones who need to supply that data. The more accurate and detailed the training data, the more accurate the OCR application’s results will be.
2. Providing Good Quality Documents: Before processing, humans need to ensure whether the data can be read properly by the OCR application. Making sure the photo is not blurry, has high resolution, sufficient lighting, and no stains is the human’s responsibility in helping the OCR analysis process.
3. Validating and Correcting Results: Before being allowed to work independently, OCR results are validated first to ensure accuracy. If there are errors, humans will edit them and retrain the system with those cases as new training data.

Conclusion

Behind the convenience it offers, OCR applications have a complex working process, from inputting documents, cleaning images so the text can be read, analyzing character patterns based on prepared training data, to refining the data so it can be stored, searched, and used alongside other data.

The human role in OCR is not only as a user, but also as a supervisor who ensures the quality of both the input and the output.