SoftRobot utilizes NLP Natural Language Processing Technology
SoftRobot's RPA integrates industry-leading NLP (Natural Language Processing) models to process unstructured text data, enabling robots to handle more complex tasks and automate customer workflows.
Address Recognition Accurately extracts address information, converts it into structured data, and generates standard location information.
Word Segmentation and Representation Processes input text, recognizes words and segments phrases to facilitate text analysis and semantic understanding.
Semantic Analysis Supports intent analysis, query extraction, and command recognition to process natural language inputs.
Dependency Syntax Analysis Automatically analyzes the syntactic structure of text, improving the understanding and processing of text content.
DNN Language Models Determines the semantic relationships between words, sentences, and texts for deeper analysis and practical applications.
Review Summarization Extracts reviews and summaries for analysis, improving decision-making efficiency.
etc...
SoftRobot combines OCR Image Recognition Technology
In RPA applications, OCR is widely used in scenarios that extract and verify data from identity document images. SoftRobot OCR is based on machine learning and deep learning technologies, combining image processing, computer vision, and natural language processing to improve recognition efficiency and accuracy significantly.
application examples are:
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General Recognition Model
Recognizes text in images, including numbers, and calculation formulas.
Form Recognition Model
Accurately extracts and processes text from physical(scanned) or digital tabular images and documents.
Card and Ticket Recognition
Supports recognition of driving licenses, marriage certificates, household registration books, bank cards, real estate certificates, business licenses, invoices, and parking tickets, etc.
