- 概述
- 入门指南
- 构建模型
- 使用模型
- ML 包
- 1040 - ML 包
- 1040 附表 C - ML 包
- 1040 附表 D - ML 包
- 1040 附表 E - ML 包
- 1040x - ML 包
- 3949a - ML 包
- 4506T - ML 包
- 709 - ML 包
- 941x - ML 包
- 9465 - ML 包
- 990 - ML 包
- ACORD125 - ML 包
- ACORD126 - ML 包
- ACORD131 - ML 包
- ACORD140 - ML 包
- ACORD25 - ML 包
- 银行对账单 - ML 包
- 提单 - ML 包
- 公司注册证书 - ML 包
- 原产地证书 - ML 包
- 检查 - ML 包
- 儿童产品证书 - ML 包
- CMS1500 - ML 包
- 欧盟符合性声明 - ML 包
- 财务报表 (Financial statements) - ML 包
- FM1003 - ML 包
- I9 - ML 包
- ID Cards - ML 包
- Invoices - ML 包
- InvoicesAustralia - ML 包
- 中国发票 - ML 包
- 希伯来语发票 - ML 包
- 印度发票 - ML 包
- 日本发票 - ML 包
- 装运发票 - ML 包
- 装箱单 - ML 包
- 工资单 - ML 包
- 护照 - ML 包
- 采购订单 - ML 包
- 收据 - ML 包
- 汇款通知书 - ML 包
- UB04 - ML 包
- 水电费账单 - ML 包
- 车辆所有权证明 - ML 包
- W2 - ML 包
- W9 - ML 包
- 公共端点
- 支持的语言
- 数据与安全性
- 许可和计费逻辑
- 如何
复选框和签名
Checkboxes and signatures are two elements that play crucial roles in various types of documents, ranging from contractual agreements to registration forms. Understanding how to correctly annotate checkboxes and signatures is important in making the most out of your model.
- Mutually exclusive checkboxes.
- Non-mutually exclusive checkboxes, where you can select more than one option.
An important aspect to consider is the number of choices offered within a given multiple-choice field. In some cases there could be a single option, where the checkbox is either checked or not. However, in many instances, there may be 10, 20, or even more options, often organized into a grid or table format, which is common for health forms.
In terms of annotating these diverse multiple-choice fields, there are four primary methods you can use.
Let's use an example to understand how you can annotate the options.
This approach has the advantage that you have a single field, which requires less data. It also doesn't depend upon the successful detection of checkboxes. For example, if a checkbox is mistakenly detected as the letter X, the model can still learn to recognize that it signifies the selection of the option next to it.
However, a potential disadvantage is the necessity to ensure that both options are roughly equally represented, which might not always be the case. For instance, if 90% of the documents in your dataset have 2018 checked, the model's performance could be affected, leading to the failure of this approach. The problem gets worse when you have more options because some of them are almost always rare. In these cases you may need to create fake documents with the rare options checked to balance things out.
In the previous example, you might have created two distinct fields: one labelled 2018 where you consistently annotate the checkbox for that year, and another one labelled 2019 where you continuously annotate the checkbox for 2019, whether it's checked or not. This method's positive aspect is that balance becomes less critical; even if one choice is selected 90% of the time, the model can still learn to identify them because the checkboxes hold fixed positions.
The downside is that you have two fields instead of one. While this may not pose a considerable issue when dealing with two options, handling 10-20 options and consequently creating 10-20 fields rather than a single one can significantly complicate the annotation process. Additionally, this also leads to a more challenging model training process, requiring more training data.
Another drawback is the occasional incorrect detection of the checkbox, which can leads to the need of more complex logic in the workflow to manage all the returned X, V, or K characters. In some cases, the OCR might even merge the checkbox with the word next to it, like X2018, requiring an even more complex RPA logic to handle this situation.
Multi-value fields make it easier to annotate, and they are not affected by imbalances in checked options or by a wide variety of selections. However, these fields are still subject to the accuracy of checkbox detection and the potential risk of checkboxes being merged with adjoining options. OCR errors are very hard to defend against.
This approach also simplifies the annotation process and is less sensitive to checkbox detection errors. However, it may be more sensitive to unbalanced options.
All of these options may be appropriate in some situations. Initially, the first option is preferred. As the accuracy of the checkbox detection in UiPath® Document OCR has improved, options two and three are preferred.
Signatures can be identified using UiPath Document OCR, allowing ML models to detect them directly.
You can annotate a signature like any other field in your document. Once the signature is identified by UiPath Document OCR, the ML model learns to recognize the field as a signature.
At inference time, the signature will be retrieved as displayed in the documents. You then have to convert this into a boolean field (Yes/No) using RPA logic.