xmrzapp 25.10.9

Extended MRZ Passport Reader From Image

MRZ Passport Reader from Image

This project is an implementation of a Machine-Readable Zone (MRZ) reader from images using segmentation, face detection, and Optical Character Recognition (OCR). It leverages TensorFlow Lite models for segmentation, a Caffe model for face detection, and EasyOCR for text recognition.

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Features

• MRZ Detection: Automatically detects and segments the MRZ region in passport images.
• Face Detection: Identifies and crops the face from the passport image.
• OCR with EasyOCR: Extracts text from the segmented MRZ region.
• Preprocessing: Optional preprocessing steps including skew correction, shadow removal, and background clearing to improve OCR accuracy.

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Installation

Prerequisites

• Python 3.10 or higher
• Git installed

Install from GitHub

git clone https://github.com/devnbugs/xmrzapp.git
cd xmrzapp
pip install -e .


## Example Usage
```python

import cv2
from xmrz_reader import MRZReader

# Initialize the MRZReader
reader = MRZReader(
    facedetection_protxt="./xmrzapp/weights/face_detector/deploy.prototxt",
    facedetection_caffemodel="./xmrzapp/weights/face_detector/res10_300x300_ssd_iter_140000.caffemodel",
    segmentation_model="./xmrzapp/weights/mrz_detector/mrz_seg.tflite",
    easy_ocr_params={"lang_list": ["en"], "gpu": False}
)

# Load an image
image_path = "path_to_your_image.jpg"

# Perform MRZ reading with optional preprocessing and face detection
text_results, segmented_image, detected_face = reader.predict(
    image_path,
    do_facedetect=True,
    preprocess_config={
        "do_preprocess": False,
        "skewness": False,
        "delete_shadow": False,
        "clear_background": False
    }
)

# Display results
print("Recognized Text:")
for bbox, text, confidence in text_results:
    print(f"Bounding Box: {bbox}")
    print(f"Recognized Text: {text}")
    print(f"Confidence: {confidence:.2f}")
    print("-" * 50)

if detected_face is not None:
    print("Face detected in the image.")

# Show images
cv2.imshow("Segmented Image", segmented_image)
if detected_face is not None:
    cv2.imshow("Detected Face", detected_face)
cv2.waitKey(0)
cv2.destroyAllWindows()

Example Result

After you give this image to the models , you will take this result P<GBRUNITED<KINGDOM<FIVE<<JODIE<PIPPA<<<<<<<1071857032GBR8501178F1601312<<<<<<<<<<<<<<02 as a string. This study is a basic solution . Your image which you give should be clear, and it should cover whole area.