Case Study
Document Digitization
AI-driven document digitization that enhances accuracy, optimize OCR, and improve data extraction for paperless accounting, achieving 95% accuracy.
AI-driven document digitization that enhances accuracy, optimize OCR, and improve data extraction for paperless accounting, achieving 95% accuracy.
The client needed an advanced solution for digitizing various accounting documents to improve their service offerings. They sought efficient extraction of crucial information from documents using artificial intelligence, particularly focusing on deep learning, natural language processing (NLP), and computer vision to enhance their platform's capabilities.
Our client is a leading provider of paperless accounting solutions, offering an innovative platform to streamline the management and digitalization of accounting documents, improving efficiency and reliability in financial operations.
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Abhay
Head of Mobile Development
> Limited data for training, particularly for specific document types and categories.
> Difficulty achieving high accuracy with object detection models.
> Extraction of data from documents of varying quality posed significant challenges for OCR.
We improved the accuracy and robustness of the Yolo model through image augmentation techniques and computer vision methods. This boosted OCR performance, enabling effective extraction of key information. Object detection was enhanced by training a real-time system, focusing on Regions of Interest (ROIs) within the documents. We also optimized the OCR algorithms and applied NLP techniques to ensure proper formatting and accuracy in the extracted data.
The project achieved 95% accuracy in detecting key fields from documents, handling almost all voucher types provided by the client. This solution significantly enhanced the client’s document processing efficiency and ensured reliable data extraction, paving the way for smoother, paperless accounting operations.
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