I’m working on an Android app and need help with text recognition. My goal is to take a photo using the device camera, convert that image into a grayscale PDF document, and then extract specific text fields from it as strings.
I’ve been looking into different approaches but I’m not sure about the best way forward. Should I handle the OCR processing locally on the device or do I need to send the image to a remote server for processing?
Has anyone implemented something similar? I would really appreciate if someone could share a basic code example showing how to:
Capture image from camera
Convert to monochrome PDF format
Extract text data from the document
Any guidance on the most efficient approach would be great. Thanks in advance for your help!
I implemented a similar feature in my production app last year and found that local processing works surprisingly well for most use cases. The key is using ML Kit’s Text Recognition API which handles the OCR directly on device without needing server calls.
For the PDF conversion part, I used iText library to generate the grayscale document after processing the camera image through OpenCV for better contrast enhancement. The tricky part was getting the image preprocessing right - you’ll want to apply some gaussian blur and threshold adjustments before feeding it to the OCR engine.
One thing I learned the hard way is that camera resolution matters significantly for text extraction accuracy. I force the camera to use at least 1080p resolution and implemented a simple focus validation check before allowing the user to capture. The processing time on modern devices is usually under 2 seconds for a typical document page.
Local processing also gives you better privacy compliance since sensitive document data never leaves the device. Just make sure you handle memory management properly when dealing with high resolution images to avoid crashes.
You might want to consider Firebase ML Kit’s on-device text recognition as your starting point since it handles most of the heavy lifting without requiring internet connectivity. I’ve built something similar for invoice processing and found that the biggest challenge isn’t actually the OCR itself but getting consistent image quality from the camera preview.
For the PDF conversion, I ended up using PDFBox-Android rather than iText because of licensing considerations in commercial applications. The workflow I settled on was capture → image preprocessing → OCR extraction → PDF generation with embedded text layer. This approach lets you maintain both the visual document and searchable text.
Regarding local versus server processing, I’d recommend starting with on-device processing first. Modern Android devices handle OCR quite well and you avoid the complexity of managing server infrastructure. You can always add cloud processing later as a fallback for edge cases where local recognition fails. The main downside is that accuracy can vary significantly between different device capabilities and lighting conditions during capture.
honestly the camera2 api can be a pain to work with but once you get it setup properly the results are solid. i’d skip the pdf conversion step entirely and just do ocr directly on the bitmap - way less overhead and processing time. tesseract4android works great if ml kit doesnt give you the accuracy you need.