They, too, require training data, and feedback and refactoring, to achieve optimal outcomes. The complexity and nuance of real-world OCR tasks gives deep learning models an appreciable performance edge.ĭeep learning models don’t train themselves. OCR models are a subset of machine learning models, and more and more, deep learning OCR is data scientists’ preferred approach. Other examples include license plate recognition, menu digitization, language translation, and many more Likewise, many industries are seeking to eliminate the need for humans to interpret and process handwritten content like patient charts, whiteboard sessions and annotated text documents Industries like real estate and financial services want to reduce or eliminate human involvement in digitizing business documents and other artifacts and electronically capturing the business-critical content therein Many autonomous device use cases demand an ability to read text in the form of signage, warnings, and surface-embedded instructions OCR projects are seeing explosive growth because of their potential for reductions in the cost of human labor and human mistakes and increases in productivity and security. Today’s OCR is an application of computer vision that enables machines to find and extract text embedded in images. ![]() I also spent many hours in the proofreading pool comparing the microfiche output to the source data, the Manhattan White Pages, and logging corrections. I learned to program the DEC VAX that drove the scanner by typing octal instructions onto paper tape and then bootstrapping the tape reader. They had an SUV-sized scanner in their computer room that digitized IBM Selectric double-spaced Pica text with about 80% accuracy and printed it to microfiche. As a teenager in the 1970s I worked for an early Optical Character Recognition (OCR) company.
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