@AbdurRehman

I focused on adding support for videos. I first implemented a basic video anonymization system so uploaded videos could be processed the same way as images. I also made sure that the audio for the video is preserved so that it carries over to the anonymized video being unchanged.
After getting the basic system working, I improved how videos are processed by adding object tracking and scene change detection. Instead of running detections on every frame, the system can now follow previously detected objects across frames and automatically run detections again when needed (when a tracker is lost, or the scene changes drastically, or periodically after a set amount of frames). This provides a great performance boost while the result stays reliable.
Next, I want to continue improving video processing by optimizing it further and making it more accurate.

I focused on improving the entire OCR system, as the previous approach with EasyOCR was missing too much text and reducing the accuracy of the entire system. After spending quite a bit of time trying to improve it, I decided to replace it with PaddleOCR. Now more accurate text detections are being produced making the entire program more robust.
After, I expanded the regex based detectors to also account for URLs in addition to emails, etc. I also improved the phone number detection logic to reduce false positives and improve overall accuracy. Then, I adjusted padding around blurred regions so that stuff is covered more consistently and without blurring unnecessary content.
The last thing I did was optimizing PaddleOCR as it was much slower than EasyOCR. After a lot of tinkering I was able to significantly reduce its processing time. For some files it now performs at roughly the same speed as EasyOCR while being way more accurate.
Next, I probably want to add support for more file types and ultimately videos. And later also support for multiple languages.

I added a new way for the app to detect personal information by using named entity recognition. Previously, the app could only find structured information like emails and phone numbers, but it could not detect things like names. So I integrated Presidio to identify such information from the text extracted from images. These extractions are then combined with the existing PII detections done using regex for the structured stuff. This improves the accuracy of the entire system by combining multiple detection methods instead of relying on one single approach.
Next, I will improve the detection process further as right now it isn’t too accurate, mostly because I think the OCR is missing some stuff from the text. So I might try replacing it or find some other way.

I implemented PII detection, allowing the app to identify and blur sensitive information within the text extracted from images instead of blurring everything blindly. First I used regular expressions to detect structured data such as email addresses, phone numbers, etc. But OCR frequently omitted punctuation, causing many valid emails to be missed. To fix this, I redesigned the everything around an OCR token graph. Each token stores its bounding box, neighboring tokens, and line information, allowing the detections to be more accurate. I might switch to PaddleOCR again or someother ocr so that I get more accurate detections. I also spent time improving the overall design of the PII detection system. These changes make it easier to extend in the future while improving the accuracy of the detected regions that are blurred.
Next, I will implement NER based detection to identify unstructured personal information such as names.

I implemented OCR-based text detection, allowing the application to detect text within uploaded images. I initially used PaddleOCR, but found that it took over 30 seconds to process a single image. After spending a lot of time trying to optimize its performance, I decided to switch to EasyOCR, which reduced the processing time to around one second while maintaining the same detection quality. Switching the OCR engines was well worth it. I also tried to keep the OCR implementation consistent with the face and license plate detectors and also optimized them too.
Next, I will implement detection of sensitive information within the extracted text using regular expressions and NER. This will allow the application to distinguish ordinary text from personal information such as names, emails, phone numbers, etc, and allow me to blur only the relevant regions (right now the the entirety of the detected text is blurred).

I expanded the application by adding support for license plate detection and blurring. The app can now detect both faces and license plates in an image and blur them.
I also made the API more flexible by allowing users to choose what they want to anonymize. Instead of always processing everything, they can now select whether to detect faces, license plates, or both. This makes the application more efficient and easier to extend with new features in the future.
I also improved the structure of the project so that adding new types of detections will require much less work. I also added unique ids to each detection, which will be useful for future features that let users blur individual detections.
Next, I will implement detection of sensitive text within images. This will allow the app to identify information such as email addresses, phone numbers, and other personal details, then blur them.

I focused on improving the backend rather than adding new features. I reorganized the API by splitting it into smaller, more focused endpoints. For example, there’s now a dedicated endpoint that only detects faces and returns their locations, while separate endpoints handle image anonymization and API health checks. This makes the API easier to use and gives me more flexibility as I add new features.
I also reorganized the detection system so that all object detection goes through a single service. At the moment it only detects faces, but it’s now much easier to add support for other types, such as license plates, without having to redesign the rest of the application.
Health check endpoints let the application report whether it’s running correctly and whether the AI models have loaded successfully.
Next I’m going to add detection beyond faces by adding license plate detection. These changes are gonna make it much easier for me to do so.

I spent some time refining the face anonymization system. The pixelation effect was completely reworked to produce smoother, better looking results while blurring the faces more accurately (using the previous blur you could sometimes make out the features of the people). I also cleaned up the implementation, making the blur functionality much simpler and easier to maintain and most importantly I made it more reusable for ease of use in the future for more features.
Behind the scenes, I also reorganized the project’s Python environment by removing unused libraries and recreating the virtual environment. Next I am planning to either add support for videos or make the API better by creating more focused routes such as one that only detects faces and other that returns the blur image. I also want to add functionality where the user can select which people to anonymize instead of blurring everybody detected.
After that, I’ll expand beyond simple face anonymization by adding support for automatically detecting and anonymizing other sensitive information, such as license plates, documents, and other personally identifiable content.

Replaced the temporary grayscale filter with automatic face anonymization. Uploaded images are now scanned for faces, and every detected face is pixelated. I also improved detection accuracy to reduce missed faces by tweaking a bunch of stuff for a while and adding more functionality to my face detection like running the image at multiple sizes. A lot of my time was also spent making the pixelated effect look good. I first went for only blurring the faces but now the face is pixelated along with a smooth transition into the surrounding image making the effect look pretty good. Right now images with large crowds are correctly blurred, including people who are far away or appear very small in the image.

Integrated OpenCV so uploaded images are now decoded, processed, and returned to the client. I also introduced modularity into the code by creating separate modules for each step. The grayscale filter is only a temporary placeholder before I implement functionality such as blurring faces.

Added image upload functionality with validation for missing files and non image uploads. The API right now only returns basic metadata about the upload. Next I’ll integrate OpenCV to begin the image processing stage.

Set up the project’s foundation by creating Flask application structure, initializing Git, creating a virtual environment, and organizing the codebase for future features. I also got the development server running to verify everything is wired up correctly. Next, I’ll implement image uploads.