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Form – Python, Libraries (NLP, Keras, Scikit-learn), Machine Learning Frameworks (CNN, RNN)

Client Profile

FORM is a mobile digital assistant for frontline teams. Their field advancement platform automates daily activities, improves and Speeds data collection, and provides executives with real-time insight to drive the right and best action possible, anywhere.

Client Industry

Data Analysis

Business Challenges

The client was looking for Sales Support Software for Beverage Companies. A software to extract data from menu cards (Bars / Restaurants) and applies it to market research of Wines, Spirits, Cocktails, and other products to achieve target metrics.
Other challenges include:

  • Statistics available in the market were based on manually collected data sources.
  • Data sources were limited.
  • Obtaining information about items on the menu card of restaurants and bars proved to be an obstacle.
  • Processing lengthy information from Multiple Restaurants/Bars caused;
    • Excessive effort with minimal outcomes.
    • Immense time.
    • Extensive human resources.
    • Unreliable data


  • Plego Technologies Developed a beverage sales support Software that uses machine learning techniques to extract text information from menu cards and segment it automatically.
  • The application processes data and forwards it to a data warehouse for additional analysis by the business intelligence or sales teams.
  • The algorithm in Software enabled executives to draw the market insights.
  • The application enabled the expansion of the information available related to Wines, Spirits, Cocktails, and the ingredients that allow beverage companies to make accurate sales and placement decisions.
  • The application is designed with multiple mathematical calculations and logic retrieving text information from the menu using Optical Character Recognition – OCR, Text Planning and Natural Language Generation – NLG.

  • The text data is further segmented with the help of Deep Learning Artificial Intelligence that includes; the Long Short-Term Memory – LSTM approach.
    Segmented data is then uniquely categorized using;

    • Convolution Neural Network – CNN to extract data from Images.
    • Recurrent Neural Network – RNN Time Series Data Format.
    • Object Detection Model – ODM, Identify the position of objectives within the Image.
  • The application employs Artificial Intelligence and Machine Learning Statistical tools to explore data and provide more accurate and effective results.
  • Insights of the Software help brands to identify customer association with;
    • Liquor.
    • Wines.
    • Beers.
    • Food ingredients.
    • Price.
    • Quantity.
    • and other factors.

  • Developers employed an Open-Source Computer Vision Library to guide and distinguish the menu’s categories, such as;
    • Drinks.
    • Beer.
    • Wine.
    • Meal sections.

  • The team at Plego integrated machine learning technologies to interpret information from menu cards successfully while eliminating manual activities, allowing Businesses to reduce the workforce and time while optimizing accurate results, enhancing the ability to produce and sell the products efficiently.

Technologies Used

Python, Libraries (NLP, Keras, Scikit-learn)Machine Learning Frameworks (CNN, RNN)

Services Provided

Application Development