{"id":2441,"date":"2020-10-13T18:15:12","date_gmt":"2020-10-13T18:15:12","guid":{"rendered":"http:\/\/digitalscientists.com\/?post_type=case_studies&p=2441"},"modified":"2024-06-13T08:12:15","modified_gmt":"2024-06-13T08:12:15","slug":"handwriting-analysis","status":"publish","type":"case-studies","link":"https:\/\/digitalscientists.com\/case-studies\/handwriting-analysis\/","title":{"rendered":"Handwriting analysis"},"content":{"rendered":"\n\n\n\n
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\n Handwriting analysis <\/h2>\n\n
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Convert handwriting into text<\/h1>\n <\/div>\n \n \n
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A billion-dollar client needed to convert thousands of handwritten names and email addresses into typed text so its marketing team could digitally store, sort, and search the contact information. The existing out-of-the-box solutions fell short in accuracy and optical character recognition, so the client came to us for a custom software development<\/a> approach to solve this data challenge.<\/p>\n <\/div>\n \n

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Training data challenge<\/h2>\n<\/div>\n\n\n\n
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Converting handwritten script to typed text continues to be one of the most vexing challenges in data science. A model that can automatically recognize and digitize handwritten notes and segment text within lists requires a complex deep learning neural network and a large dataset composed of millions of handwriting samples. Our\u00a0AI software development<\/a> and machine learning experts\u00a0took up the gauntlet.<\/p>\n\n\n

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