I trained my model on 5 images but the accuracy is not particularly great.
Link to form: https://imgur.com/a/BOHVG7G
JSON Output:
{
"status": "success",
"pages": [
{
"number": 1,
"height": 1055,
"width": 1225,
"clusterId": 0,
"keyValuePairs": [
{
"key": [
{
"text": "Name:",
"boundingBox": [
163.7,
987.1,
242.2,
987.1,
242.2,
963.4,
163.7,
963.4
]
}
],
"value": [
{
"text": "Luca Bassi",
"boundingBox": [
365.3,
982.1,
458.8,
982.1,
458.8,
963.4,
365.3,
963.4
],
"confidence": 1
},
{
"text": "Brassi",
"boundingBox": [
365.3,
938.7,
417.1,
938.7,
417.1,
919,
365.3,
919
],
"confidence": 1
}
]
},
{
"key": [
{
"text": "Surname:",
"boundingBox": [
166.9,
937.8,
282.8,
937.8,
282.8,
913.1,
166.9,
913.1
]
}
],
"value": [
{
"text": "19 Cider Lane",
"boundingBox": [
367.3,
719.1,
490.4,
719.1,
490.4,
698.4,
367.3,
698.4
],
"confidence": 0.8
}
]
},
{
"key": [
{
"text": "e-Mail Address:",
"boundingBox": [
164.7,
893.4,
358.1,
893.4,
358.1,
867.8,
164.7,
867.8
]
}
],
"value": [
{
"text": "brassi@brassi.com",
"boundingBox": [
364.3,
893.4,
528,
893.4,
528,
867.8,
364.3,
867.8
],
"confidence": 0.6
}
]
},
{
"key": [
{
"text": "Phone Number:",
"boundingBox": [
163.7,
849.1,
361.1,
849.1,
361.1,
822.6,
163.7,
822.6
]
}
],
"value": [
{
"text": "456-3456",
"boundingBox": [
367.3,
849.1,
451.8,
849.1,
451.8,
822.6,
367.3,
822.6
],
"confidence": 1
}
]
},
{
"key": [
{
"text": "Mobile Number:",
"boundingBox": [
164.7,
803.8,
361.1,
803.8,
361.1,
777.3,
164.7,
777.3
]
}
],
"value": [
{
"text": "456-2135",
"boundingBox": [
366.3,
803.8,
450.8,
803.8,
450.8,
777.3,
366.3,
777.3
],
"confidence": 1
}
]
},
{
"key": [
{
"text": "Street:",
"boundingBox": [
166.9,
714.1,
246.2,
714.1,
246.2,
690.5,
166.9,
690.5
]
}
],
"value": []
},
{
"key": [
{
"text": "House:",
"boundingBox": [
163.7,
668.8,
250.2,
668.8,
250.2,
645.3,
163.7,
645.3
]
}
],
"value": [
{
"text": "Detroit",
"boundingBox": [
364.3,
628.5,
427.3,
628.5,
427.3,
609.7,
364.3,
609.7
],
"confidence": 0.6
}
]
},
{
"key": [
{
"text": "Town:",
"boundingBox": [
166.9,
623.5,
241.2,
623.5,
241.2,
598.9,
166.9,
598.9
]
}
],
"value": [
{
"text": "80012",
"boundingBox": [
365.3,
585.2,
418.1,
585.2,
418.1,
565.5,
365.3,
565.5
],
"confidence": 1
}
]
},
{
"key": [
{
"text": "Postcode:",
"boundingBox": [
164.7,
580.2,
286.8,
580.2,
286.8,
554.5,
164.7,
554.5
]
}
],
"value": [
{
"text": "Russia",
"boundingBox": [
365.3,
534.8,
417.1,
534.8,
417.1,
516.2,
365.3,
516.2
],
"confidence": 0.6
}
]
},
{
"key": [
{
"text": "Comments:",
"boundingBox": [
166.9,
487.7,
305.2,
487.7,
305.2,
464,
166.9,
464
]
}
],
"value": [
{
"text": "The quick brown fox",
"boundingBox": [
366.3,
485.7,
549.4,
485.7,
549.4,
464,
366.3,
464
],
"confidence": 0.6
}
]
}
],
"tables": []
}
],
"errors": []
}
As you can see Surname and the address stuff doesn't really come out so well. Is there a way to train this more effectively or do I need to just use a larger data set?
I dug around in azure's portal but I am not really sure if I over looked an option to train this better.
I created a program in Python with opencv and matplotlib to inspect your result, then I found the Surname and e-Mail Address both come out, but the House and Country not, as the figure below.
Here is my code for drawing.
import cv2
import matplotlib.pyplot as plt
import json
import numpy as np
json_file = open('sample.json')
json_dict = json.load(json_file)
page = json_dict['pages'][0]
height = page['height']
keyValuePairs = page['keyValuePairs']
key_boundingBoxes = [np.int64(key['boundingBox']) for keyValuePair in keyValuePairs for key in keyValuePair['key']]
key_texts = [key['text'] for keyValuePair in keyValuePairs for key in keyValuePair['key']]
value_texts = [value['text'] for keyValuePair in keyValuePairs for value in keyValuePair['value']]
print(key_texts)
value_boundingBoxes = [np.int64(value['boundingBox']) for keyValuePair in keyValuePairs for value in keyValuePair['value']]
img = cv2.imread("sample.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
[cv2.rectangle(img, (boundingBox[0], height - boundingBox[1]),(boundingBox[4], height - boundingBox[5]) ,(0,255,0), 3) for boundingBox in key_boundingBoxes]
[cv2.rectangle(img, (boundingBox[0], height - boundingBox[1]),(boundingBox[4], height - boundingBox[5]) ,(255,0,0), 3) for boundingBox in value_boundingBoxes]
plt.figure()
plt.imshow(img)
plt.axis('off')
plt.show()
Ofcourse, it's not related to improve the accuracy.
Per my experience, easily to improve the accuracy is to feed the training model with more images, because you were using Azure Cognitive Service which algorithm you can not change.
来源：https://stackoverflow.com/questions/57662250/how-to-improve-the-accuracy-of-form-recognizer