The Optical Character Recognition (OCR) model is a text-recognition model that identifies and extracts text (both printed and handwritten) from digital images. You can train the model to scan a digital image and extract only the required information by using machine learning. This is especially useful when you want to process and retrieve structured data from a large volume of unstructured data.
This structured data can then be stored and processed by the businesses whenever required, thereby helping them simplify and automate their data entry processes. For example, structured data is the date and time of an email, whereas unstructured data is the entire content of the email itself.
Creator supports two types of OCR models: you can build custom models suited to your business needs, or choose a ready to use (prebuilt) model that is ready to deployed in your applications for many common business scenarios.
You can build custom OCR models that can be trained to identify and extract only the required values. The custom OCR model utilizes an ensemble of industry leading text recognition technologies to identity and highlight the text in the case of custom OCR models. All the extractable text identified by the model will be now be highlighted to show that they're untagged values. You can then add and tag the fields whose values you want to extract from the images. The model can then be trained to extract and process the required text found in your images.
The following GIF shows the required text values being extracted from the input image (Invoice):
You can build and train custom OCR models tailored to suit your business needs. Additionally, you can utilize our ready-to-use OCR model, which can be directly deployed into your applications.
Let's say you want to extract text from a certain set of input images. In this case, a custom OCR model would be better suited. In some cases, you might want to extract all the detected text from the input image. The ready-to-use OCR model can be used in this case.
Let's assume that you've built Zylker's Invoice Processing app using Creator. You've a form named Invoice Details, in which you add the details of your invoice along with a digital copy of those invoices. You need to extract certain data from the invoice such as the invoice date, invoice number, due date, and the billing address. This can be done manually by relying on paper invoices to process payments and maintain accounts. However, when multiple entries are involved, automating the extraction process saves a great deal of time and manual work.
Here's how you would use the OCR model in the above case:
Training data is the initial dataset that is used by the model to analyze data patterns, make interpretations, and arrive at a conclusion that helps it recognize text from images. To train an OCR model, you need to gather sufficient images of similar and different layouts. Next, you need to identify the values that you want to extract from the images you've gathered. Once the training data has been finalized, you can proceed to add fields to your OCR model.
In Creator, form fields store the values entered by users. Similarly, the values that you want to extract will be displayed in the respective fields. Adding fields is used establish their definition them so that when the model is implemented in your Creator application, these fields will be listed in that application's form. You can select/deselect the pre-defined fields as required. Now that you've identified the values you need to extract from your training data, you need to add fields and their corresponding data types.
The images that you gathered earlier will come in handy now. The images can be pictures of documents that include bills, checks, invoices, passports, receipts, and so on. The text in these documents can be handwritten and/or printed, although printed text is preferable.
Once you've uploaded the required images, the text in each of the uploaded images will appear highlighted. Next, you need to tag values for the fields you'd added earlier in all the uploaded images. Tagging here refers to mapping or associating the added field to the value it must extract and display. You can tag a value by selecting and dragging over the corresponding value in all the images. For example, if you've added an Invoice Number field whose datatype is number, you must tag the value of the invoice number in the image. This is done so that the OCR model recognizes that these are the field values that need to be extracted from the input data.
Language (for print and handwritten text)
After adding the training data, you can review the model details such as Model Name, Model Type, training data, and number of images added. If you need to make any modifications, you can go back and make them. Otherwise, you can proceed to train the model.
Before you can actually use your OCR model in your application, you have to train it to perform the way you want. After you've selected and reviewed your training data, click Train to train your model.
Once training your model is complete, you can view the model details, the model's versions, and its deployment details, if any. The model is now ready to be published and deployed into your apps.
You can manage your model in the following ways:
After you've trained, tested, and evaluated your model, you can tweak your model to improve its performance. Now here are some things you can try to help improve your model's performance.
After you train your model, you can publish it to make it available to your users and start making predictions. Learn how
To use your OCR model in an environment-enabled Creator application, you must have at least one version of that application published in the production environment. After deploying your model in the application, you can filter between different stages of the environment to check which stage is the model deployed in. Learn how
After you've published your model, you need to select the application and form in which you want to deploy your model. You'll be redirected to the chosen form, where the fields that you defined earlier will be listed in the form builder. You can select/deselect the fields as required.The fields that have been deselected will not be added in that form. For example, you might be using the same OCR model in two forms. You might not require the same set of fields in both the forms and can deselect the ones that are not required.
A new OCR field will be added in your form, in which you can upload an image of your choice. The OCR model will analyze and display the extracted values in the defined fields.
To help you get started quickly with OCR, we provide sample data that you can readily use in your Creator applications. You can scroll down and download the attachment
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