Our project aimed to tackle a significant issue faced by legal assistants: the immense amount of time spent inputting data from client consultation forms into immigration forms. This manual and monotonous task consumed a substantial portion of their workday, hindering their ability to focus on more critical legal tasks.
Our solution was designed to automate this tedious process, liberating legal professionals from the shackles of paperwork. We leveraged Amazon Web Services (AWS) to create an efficient and accurate solution. The main technologies we have utlisied are:
We utilized AWS S3 to store all the client files, which included handwritten consultation forms and evidence documents. This served as the repository for the information we needed to extract.
To extract essential data from the images and handwritten documents, we employed AWS Textract. This powerful service allowed us to analyze the images and convert them into machine-readable text.
With the extracted data in hand, we employed a Python library called Fitz. This library enabled us to take the relevant information and seamlessly populate the immigration forms.
While building this solution, we encountered several challenges. One significant obstacle was transferring the extracted information from the Lambda function back to Python for further processing. Additionally, aligning the extracted data with the appropriate fields in the immigration forms proved to be a complex task. However, through watching AWS videos and other tutorials, we overcame these challenges, ensuring the successful functioning of our solution.
Our project not only streamlines the workflow for legal professionals but also has the potential to revolutionize data entry tasks in various other fields, such as medical receptionists and bank administrative jobs, where similar time-consuming and repetitive tasks exist.
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