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In today’s digital age, Optical Character Recognition (OCR) has become an indispensable tool for converting handwritten or printed text into digital format. With the vast amount of text generated daily, OCR offers a convenient and efficient solution for digitizing documents and facilitating data processing. OCR technology works by training computers to recognize different letters, numbers, and symbols. Once trained, the OCR system can accurately identify characters in new images. This capability has numerous practical applications, such as converting historical documents, digitizing scanned images, and improving text accessibility for visually impaired individuals. This research study integrates machine learning techniques like K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to enable high levels of accuracy and adaptability in OCR systems. This research highlights the significance of OCR in the digital world and its potential to streamline various tasks and improve efficiency.

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ScanSense: An Optical Character Recognition (OCR) Using K-Nearest Neighbors (KNN)

  • 会议论文

  • 发表日期：

    2025年

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  • Siddharth S. Pai,
  • Om Bhayde,
  • V. G. Narendra,
  • G. Shivaprasad

摘要

In today’s digital age, Optical Character Recognition (OCR) has become an indispensable tool for converting handwritten or printed text into digital format. With the vast amount of text generated daily, OCR offers a convenient and efficient solution for digitizing documents and facilitating data processing. OCR technology works by training computers to recognize different letters, numbers, and symbols. Once trained, the OCR system can accurately identify characters in new images. This capability has numerous practical applications, such as converting historical documents, digitizing scanned images, and improving text accessibility for visually impaired individuals. This research study integrates machine learning techniques like K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to enable high levels of accuracy and adaptability in OCR systems. This research highlights the significance of OCR in the digital world and its potential to streamline various tasks and improve efficiency.