On October 14, 2025, Dynamsoft unveiled its most significant leap in computer vision yet — the AI-powered Dynamsoft Capture Vision (DCV) 3.2.1000. This release introduces groundbreaking deep learning models that transform how 1D barcodes and MRZs are detected and decoded, pushing the boundaries of accuracy, performance, and real-world reliability. In this article, we will walk through the process of evaluating these new capabilities using publicly available datasets and a Python-based comparison tool we previously developed.
Demo Video
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Blurred 1D Barcode Recognition
-
MRZ Recognition
Prerequisites
AI-Powered Barcode and MRZ Detection
The core highlights of this release include:
- Barcode: Deep neural network–based localization and decoding models designed to handle blurred, low-resolution, or partially damaged barcodes.
- MRZ: A specialized deep learning model that delivers 42.7% faster processing with improved region detection accuracy for passport and ID recognition workflows.
Updating the Comparison Tool for MRZ
We previously developed a comparison tool to benchmark barcode recognition performance.
To extend this tool for MRZ evaluation, we made the following updates.
1. Add a Dropdown Button to Switch Between Barcode and MRZ Modes
mode_label = QLabel("Detection Mode:")
toolbar_layout.addWidget(mode_label)
self.detection_mode_combo = QComboBox()
self.detection_mode_combo.addItems(["Barcode", "MRZ"])
self.detection_mode_combo.setCurrentText("Barcode")
self.detection_mode_combo.currentTextChanged.connect(self.on_detection_mode_changed)
toolbar_layout.addWidget(self.detection_mode_combo)
def on_detection_mode_changed(self, mode: str):
self.current_detection_mode = mode
self.status_bar.showMessage(f"Detection mode changed to: {mode}")
if hasattr(self, 'processing_thread') and self.processing_thread and self.processing_thread.isRunning():
self.processing_thread.terminate()
self.processing_thread.wait(1000)
self.processing_thread = None
self.progress_bar.setVisible(False)
self.results.clear()
self.results_table.setRowCount(0)
self.image_files.clear()
self.file_list.clear()
self.new_files = []
self.image_comparison.sdk1_scene.clear()
self.image_comparison.sdk2_scene.clear()
self.image_comparison.summary_label.setText(f"Detection mode: {mode} - Add images to begin comparison")
self.image_comparison.sdk1_results_text.clear()
self.image_comparison.sdk2_results_text.clear()
if mode == "Barcode":
self.results_table.setHorizontalHeaderLabels(["Image", "SDK 1", "SDK 2", "Barcode Δ", "Speed Δ"])
elif mode == "MRZ":
self.results_table.setHorizontalHeaderLabels(["Image", "SDK 1", "SDK 2", "MRZ Δ", "Speed Δ"])
2. Update the Python Script to Handle MRZ Mode in the Subprocess
script_content = f'''#!/usr/bin/env python3
import sys
import json
import time
import os
from dynamsoft_capture_vision_bundle import *
LICENSE_KEY = "LICENSE-KEY"
def process_image(image_path, detection_mode):
# Initialize license
error_code, error_message = LicenseManager.init_license(LICENSE_KEY)
if error_code not in [EnumErrorCode.EC_OK, EnumErrorCode.EC_LICENSE_CACHE_USED]:
return {{"success": False, "error": f"License error: {{error_message}}", "detection_mode": detection_mode}}
# Process image
cvr = CaptureVisionRouter()
# Select template based on detection mode
if detection_mode == "Barcode":
template = EnumPresetTemplate.PT_READ_BARCODES.value
elif detection_mode == "Document":
template = EnumPresetTemplate.PT_DETECT_AND_NORMALIZE_DOCUMENT.value
elif detection_mode == "MRZ":
template = "ReadPassportAndId"
else:
template = EnumPresetTemplate.PT_READ_BARCODES.value # Default
try:
cvr.capture(image_path, template) # Trigger model loading the first time
except Exception as e:
pass # Ignore first capture errors for model loading
start_time = time.time()
result = cvr.capture(image_path, template)
processing_time = time.time() - start_time
error_code = result.get_error_code()
items = result.get_items()
# Error -10005 often just means "no results found" for any detection mode
# This is not necessarily a failure, just empty results
if error_code != EnumErrorCode.EC_OK:
if error_code == -10005:
# No results found - return empty results but success
pass # Continue with empty items list
else:
return {{"success": False, "error": f"Capture error: {{error_code}}", "detection_mode": detection_mode}}
# Initialize result containers
barcodes = []
mrz_results = []
document_results = []
# Process items based on detection mode
for item in items:
item_type = item.get_type()
if detection_mode == "Barcode" and item_type == 2: # Barcode item (CRIT_BARCODE = 2)
barcode_data = {{
"text": item.get_text(),
"format": item.get_format_string(),
"confidence": getattr(item, 'get_confidence', lambda: 0.0)()
}}
# Get location points
try:
location = item.get_location()
if location and hasattr(location, 'points'):
barcode_data["points"] = [
[int(location.points[0].x), int(location.points[0].y)],
[int(location.points[1].x), int(location.points[1].y)],
[int(location.points[2].x), int(location.points[2].y)],
[int(location.points[3].x), int(location.points[3].y)]
]
else:
barcode_data["points"] = []
except:
barcode_data["points"] = []
barcodes.append(barcode_data)
elif detection_mode == "MRZ" and item_type == 4: # Text line item (CRIT_TEXT_LINE = 4)
text = item.get_text()
mrz_data = {{
"text": text,
"parsed_info": {{}}, # Could be expanded to parse MRZ fields
"points": []
}}
# Get location points
try:
location = item.get_location()
if location and hasattr(location, 'points'):
mrz_data["points"] = [
[int(location.points[0].x), int(location.points[0].y)],
[int(location.points[1].x), int(location.points[1].y)],
[int(location.points[2].x), int(location.points[2].y)],
[int(location.points[3].x), int(location.points[3].y)]
]
except:
mrz_data["points"] = []
mrz_results.append(mrz_data)
return {{
"success": True,
"detection_mode": detection_mode,
"processing_time": processing_time,
"barcodes": barcodes,
"mrz_results": mrz_results,
}}
if __name__ == "__main__":
detection_mode = sys.argv[2] if len(sys.argv) > 2 else "Barcode"
result = process_image(sys.argv[1], detection_mode)
print(json.dumps(result))
'''
Note: The first call to cvr.capture() triggers AI model loading. To measure true image-processing time, the script calls cvr.capture() twice.
Dataset Preparation and Testing
To evaluate the new AI-powered models, we used two public datasets:
Barcode dataset
We used the Dataset3 folder (images at 1152×864 resolution, taken with an older Nokia 7610 phone lacking autofocus) to benchmark 1D barcode recognition.
MRZ dataset
Reference
K.B. Bulatov, E.V. Emelianova, D.V. Tropin, N.S. Skoryukina, Y.S. Chernyshova, A.V. Sheshkus, S.A. Usilin, Z. Ming, J.-C. Burie, M. M. Luqman, V.V. Arlazarov: “MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis”, Computer Optics (submitted), 2021.
We used the aze_passport folder for MRZ recognition benchmarking.
Source Code
https://github.com/yushulx/python-barcode-qrcode-sdk/edit/main/examples/official/comparison_tool
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