Question

我在5张图像上训练了模型，但准确性不是特别高。

JSON输出：


{
  "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": []
}

正如您所看到的，姓氏和地址信息的确不是那么好。有没有一种方法可以更有效地进行训练，还是我只需要使用更大的数据集？

我在azure的门户网站上进行了挖掘，但我不确定我是否过分选择了一种可以更好地进行培训的选项。

Answer 1

我用opencv和matplotlib用Python创建了一个程序来检查您的结果，然后发现Surname和e-Mail Address都出来了，但是{{1 }}和House，如下图所示。

这是我的绘图代码。

Country

当然，这与提高准确性无关。

根据我的经验，轻松提高准确性是为训练模型提供更多图像，因为您使用的是Azure Cognitive Service，您无法更改该算法。

如何提高表格识别器的准确性？

1 个答案: