Preeti
Document classification is a critical task for organizations dealing with large volumes of documents. Optical Character Recognition (OCR), Artificial Intelligence (AI), and Machine Learning (ML) technologies have revolutionized this process, making it more efficient and accurate. OCR converts images of text into machine-readable formats, enabling data extraction from scanned documents. AI and ML automate document classification, improving efficiency and accuracy.
AI and ML algorithms analyze data patterns to classify documents based on content, format, or metadata. They can adapt and improve classification accuracy over time, reducing errors. NLP techniques help analyze text semantics, while ML algorithms extract text from unstructured data for classification. Deep learning models like CNNs and RNNs enhance document classification accuracy.
To implement OCR, AI, and ML for document classification, steps include data collection, preprocessing, OCR implementation, feature extraction, model training, and evaluation. Deep learning models and NLP techniques will continue to advance document classification. Hybrid approaches combining different techniques and transfer learning for pre-trained models will also emerge. Improved OCR accuracy and explainable AI will enhance document classification processes.
Overall, document classification using OCR, AI, and ML offers benefits like improved data accuracy, cost savings, efficiency, and enhanced collaboration. KlearStack, an AI-powered document intelligence company, provides solutions for document classification using these technologies.
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Source link: https://medium.com/@klear-stack/document-classification-using-ocr-ai-ml-ea219fe76b9e?source=rss——ai-5
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