Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images, such as scanned documents, photographs, bills, etc. The process involves analyzing the image and identifying the individual characters within it and then converting those characters into machine-readable text. OCR software can be used to automate tasks such as document scanning, business automation, and accessibility technology. OCR software uses complex algorithms and pattern recognition techniques to identify and extract text. OCR technology has evolved over time and now it has the ability to recognize text in multiple languages and different fonts.

OCR software have been critical to businesses looking to grow quickly by leveraging digital workflows & automated processes. OCR software automate data capture from scanned documents/images and digitize the data in convenient, editable formats that fit into organizational workflows. Scanning & processing documents such as invoices, receipts, and images for valuable data has traditionally been a manual process fraught with errors and delays. OCR software solutions help businesses save time and resources that would otherwise be spent on data entry & manual validation/verification. Modern OCR software are fast, accurate and can handle common document processing constraints such as poorly formatted scans, handwritten documents, low quality images/scans, and blemishes that would have traditionally required extended manual interventions. More and more organizations are automating document processing workflows to go paperless and leverage cloud-based digital solutions that improve bottom lines.

Whether it's auto-extracting information from a scanned receipt for an expense report or translating a foreign language using your phone's camera, optical character recognition (OCR) technology can seem mesmerizing. And while it seems miraculous that we have computers that can digitize analog text with a degree of accuracy, the reality is that the accuracy we have come to expect falls short of what's possible. And that's because, despite the perception of OCR as an extraordinary leap forward, it's actually pretty old-fashioned and limited, largely because it's run by an oligopoly that's holding back further innovation. OCR's precursor was invented over 100 years ago in Birmingham, England by the scientist Edmund Edward Fournier d'Albe. Wanting to help blind people "read" text, d'Albe built a device, the Optophone, that used photo sensors to detect black print and convert it into sounds.

The New York Public Library is participating in the Chronicling America initiative to develop an online searchable database of historically significant newspaper articles. Microfilm copies of the papers are scanned and high resolution OCR software is run on them. The text from the OCR provides a wealth of data and opinion for researchers and historians. However, the categorization of articles provided by the OCR engine is rudimentary and a large number of the articles are labeled ``editorial" without further categorization. To provide a more refined grouping of articles, unsupervised machine learning algorithms (such as K-Means) are being investigated. The K-Means algorithm requires tuning of parameters such as the number of clusters and mechanism of seeding to ensure that the search is not prone to being caught in a local minima. We designed a pilot study to observe whether humans are adept at finding sub-categories. The subjective labels provided by humans are used as a guide to compare performance of the automated clustering techniques. In addition, seeds provided by annotators are carefully incorporated into a semi-supervised K-Means algorithm (Seeded K-Means); empirical results indicate that this helps to improve performance and provides an intuitive sub-categorization of the articles labeled ``editorial" by the OCR engine.