The PixLab Document Scanner, development team is pleased to announce that is now fully support scanning Emirates (UAE) ID & Residence Cards via the /DOCSCAN API endpoint at real-time using your favorite programming language.

When invoked, the /DOCSCAN HTTP API endpoint shall Extract (crop) any detected face and transform the raw UAE ID/Residence Card content such as holder name, nationality, ID number, etc. into a JSON object ready to be consumed by your app.

Below, a typical output result of the /DOCSCAN API endpoint for an Emiratis (UAE) ID card input sample:

The code samples used to achieve such result are available to consult via the following gists:

• Python Code Samples for Scanning UAE ID Card: uae_emirates_id_card_scan.py
• PHP Code Samples for Scanning UAE ID Card: uae_emirates_id_card_scan.php
• PixLab Github Repository: github.com/symisc/pixlab

The same logic applies to scanning official travel documents like Visas, Passports, and ID Cards from many others countries in an unified manner, regardless of the underlying programming language used on your backend (Python, PHP, Ruby, JS, etc.) thanks to the DOCSCAN API endpoint as shown in previous blog posts:

• Passports & Travel Document Scan: Blog Announcement & Code Sample.
• Malaysia & Singapore ID Card Scan: Blog Announcement & Code Sample.
• Aadhar India ID Card Scan: Blog Announcement & Code Sample.

Algorithm Details

Internally, PixLab's document scanner engine is based on PP-OCR which is a practical ultra-lightweight OCR system, mainly composed of three parts: DB text detection, detection frame correction, and CRNN text recognition. DB stands for Real-time Scene Text Detection.

The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module.

In PP-OCR, Differentiable Binarization (DB) is used as text detector which is based on a simple segmentation network. It integrates feature extraction and sequence modeling. It adopts the Connectionist Temporal Classification (CTC) loss to avoid the inconsistency between prediction and label.

The algorithm is further optimized in five aspect where the detection model adopts the CML (Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts the LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement, which further improves the inference speed and prediction effect.

Here at PixLab, we recently deployed on production, a brand new gender/age classification model available to our customers via the FACEMOTION API endpoint.

• The new model implementation is based on the ResNet-50 convolutional neural network (CNN) that is 50 layers deep. The network can easily classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

• The reference, implementation paper is from: Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019 (https://arxiv.org/abs/1801.07698).

• The Python/PHP code samples listed below should be able to easily output the age estimation, gender, and emotion pattern by just looking at the facial shape of any present human face in a given picture or video frame using our new classifcation model.

Python Code

• The PHP usage sample can be viewed here or on the PixLab Github repository.

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• FACEMOTION is the sole endpoint needed to perform such a task. It should output the rectangle coordinates for each detected human face that you can pass verbatim if desired to other processing endpoints like CROP or MOGRIFY plus the age estimation, gender and emotion pattern of the target face based on its facial shape.
• Finally, all of our production ready, code samples are available to consult at our samples page or the PixLab Gihtub repository.

The PixLab Computer Vision team is pleased to announce the immediate availability of PixLab Annotate. A web based image annotation, labeling & segmentation tool for Machine Learning model training tasks and beyond...

Annotate Features Set:

• Rectangle, Polygon, Zoom & Drag labeling tool.
• Consistent JSON output accepted by most Machine Learning frameworks.
• Optimized for instance segmentation (Mask R-CNN, etc).
• Client-side persistent storage - No data transfer involved.
• Persistent & easy label management (Create, Modify & Delete).
• Full screen display & Snapshot capture.

At PixLab, we really believe that annotate is a great fit for data scientists, developers or students looking for a straightforward, online image segmentation and labeling tool for their daily machine learning model training tasks and beyond...

Annotate Homepage

• annotate.pixlab.io

Feature & Support Requests

• PixLab Github Repository

The PixLab Computer Vision Team is pleased to introduce the Pixel Generate API endpoint (/pixelgenerate) which let you in a single call, generate on the fly, images filled with random pixels of desired width & height using a mix of standard Image Processing and soon Machine Learning algorithms.

This endpoint is similar to /newimage except that the image contents is filled with random pixels. This is very useful for generating background (negative) samples for feeding Machine Learning training algorithms for example.

By default, this endpoint return a JSON object holding a link to the generated image output. But, you can set it via the Blob parameters to return the image binary contents instead.

Below, a Python snippet which generate on the fly a new image of height & width of 300x300 filled with random pixels using a single call to /pixelgenerate:

The code sample used to achieve such result is available to consult via the following Github link:

• Python code for generating random pixels: pixel_generate.py.
• The endpoint documentation is available to consult at pixlab.io/cmd?id=pixelgenerate.
• Others code samples including Passports & ID Scanning ,Face Blurring, etc. and for a general introduction to the PixLab API, refer to our Github Repository and Examples Page.