def infer(model, fnImg, correct):
""" Recognize text in image provided by file path """
img = preprocessor(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE),
imgSize=Model.imgSize)
if img is None:
print("Image not found")
return
recognized = model.predict(fnImg)
print(recognized)
recognized = model.decoderToText(recognized)
print("Without Correction: ", recognized)
print("With Correction: ", correct_sentence(recognized))
if correct:
return correct_sentence(recognized)
else:
return recognized
def infer(model, fnImg):
""" Recognize text in image provided by file path """
img = preprocessor(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE), imgSize=Model.imgSize)
if img is None:
print("Image not found")
imgs = load_different_image()
imgs = [img] + imgs
batch = Batch(None, imgs)
recognized = model.inferBatch(batch) # recognize text
print("Without Correction", recognized[0])
print("With Correction", correct_sentence(recognized[0]))
return recognized[0]
def infer(model, fnImg):
""" Recognize text in image provided by file path """
img = preprocess(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE),
imgSize=Model.imgSize)
if img is None:
print("Image not found")
imgs = load_different_image()
imgs = [img] + imgs
batch = Batch(None, imgs)
recognized = model.inferBatch(batch) # recognize text
print("\n\n\n\n\nWithout Correction: ", recognized[0])
ans = correct_sentence(recognized[0])
print("\nWith Correction: ", ans)
print("\n\n\n\n\n")
f = open(r'C:\\Users\\ISHIKA\\Desktop\\EAD Project\\NTNLine/output.txt',
'w')
f.write(ans)
return ans