A package for reading id and name on KTP and SIM
Project description
Indo OCR Army
This is a package for OCR Identity card in Indonesia.
for comprehensive use, you can do this:
import os
import cv2
import matplotlib.pyplot as plt
from IndoOCRArmy.modelOCR import numericalDetectron2, boundingBoxesDetectron2, alphabeticalDetectron2, easypredict
from IndoOCRArmy.drawer import DrawOCR
from config import cfg
# load classes
drawer_ocr = DrawOCR(cfg['drawOCR'])
bBoxDet = boundingBoxesDetectron2(cfg['boundingBoxesDetectron2'])
numDet = numericalDetectron2(cfg['numericalDetectron2'])
alphaDet = alphabeticalDetectron2(cfg['alphabeticalDetectron2'])
# load image
image_ktp = cv2.imread("assets/ktp_example.jpg")
image_sim = cv2.imread("assets/sim_example.jpg")
# detect boundingboxes
crops, boxes, labels = bBoxDet.predict(image_ktp, input_type='ktp')
# detect number and alphabet
dict_ID = numDet.predict_ensemble(crops[0])
dict_Name = alphaDet.predict_ensemble(crops[1])
# choose `weighted_hardvote_word` for the best result according to our benchmark
ID = dict_ID.get("weighted_hardvote_word")
Name = dict_Name.get("weighted_hardvote_word")
# parse NIK to get information about : location, gender, and birthdate
parse_NIK = numDet.parse_nik(ID)
# create listdata and listlabel for visualization later
listdata = [ID, Name]
listlabel = [x for x in list(labels.values()) if x is not None]
for label, data in parse_NIK.items():
listdata.append(data)
listlabel.append(label)
print(ID)
print(Name)
drawer_ocr.show_list_images(list_img=crops.values())
For comprehensive result, visualize this:
drawer_ocr.show_desc(image_ktp, boxes, labels, listdata, listlabel)
For quick result, use this:
from IndoOCRArmy.modelOCR import numericalDetectron2, boundingBoxesDetectron2, alphabeticalDetectron2, easypredict
import cv2
# load classes
drawer_ocr = DrawOCR(cfg['drawOCR'])
bBoxDet = boundingBoxesDetectron2(cfg['boundingBoxesDetectron2'])
numDet = numericalDetectron2(cfg['numericalDetectron2'])
alphaDet = alphabeticalDetectron2(cfg['alphabeticalDetectron2'])
image_ktp = cv2.imread("assets/ktp_example.jpg")
easypredict(image_ktp, bBoxDet, numDet, alphaDet, input_type='ktp')
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