OCR (Optical Character Recognition) is a technology that enables the conversion of document types such as scanned paper documents, PDF files or pictures taken with a digital camera into editable and searchable data. OCR creates words from letters and sentences from words by selecting and separating letters from images. If you don't have any prior knowledge, I can recommend it. This is a slightly polished and packaged version of the Keras CRNN implementation and the published CRAFT text detection model. It provides a high level API for training a text detection and OCR pipeline.

In today's information age, impacts of digital transformations are present across varying industries and sectors.

Effects of Optical Character Recognition (OCR) quality on historical information retrieval have so far been studied in data-oriented scenarios regarding the effectiveness of retrieval results. Such studies have either focused on the effects of artificially degraded OCR quality (see, e.g., [1-2]) or utilized test collections containing texts based on authentic low quality OCR data (see, e.g., [3]). In this paper the effects of OCR quality are studied in a user-oriented information retrieval setting. Thirty-two users evaluated subjectively query results of six topics each (out of 30 topics) based on pre-formulated queries using a simulated work task setting. To the best of our knowledge our simulated work task experiment is the first one showing empirically that users' subjective relevance assessments of retrieved documents are affected by a change in the quality of optically read text. Users of historical newspaper collections have so far commented effects of OCR'ed data quality mainly in impressionistic ways, and controlled user environments for studying effects of OCR quality on users' relevance assessments of the retrieval results have so far been missing. To remedy this The National Library of Finland (NLF) set up an experimental query environment for the contents of one Finnish historical newspaper, Uusi Suometar 1869-1918, to be able to compare users' evaluation of search results of two different OCR qualities for digitized newspaper articles. The query interface was able to present the same underlying document for the user based on two alternatives: either based on the lower OCR quality, or based on the higher OCR quality, and the choice was randomized. The users did not know about quality differences in the article texts they evaluated. The main result of the study is that improved optical character recognition quality affects perceived usefulness of historical newspaper articles significantly. The mean average evaluation score for the improved OCR results was 7.94% higher than the mean average evaluation score of the old OCR results.

AI-powered optical character recognition lets insurers unlock vast troves of data and streamline all processes.||Insurers still struggle with PDFs, images and handwritten documents. Countless human hours are required to manually extract the data into a machine-readable format. This process is known as ETL (extract, transform and load). Insurers that can maximize their ETL capabilities have a powerful competitive advantage. 

Referred also as text recognition, the technology of OCR uses a scanner to convert the physical documents or images containing printed, typed or handwritten text into digitized text data that can be machine-readable. The OCR software converts the scanned images into a black and white version wherein black color represents the characters and white the background. With the help of pattern recognition to recognize the characters or feature recognition to detect the lines and strokes of the characters, characters are identified and converted into ASCII codes that can be easily handled by computer systems. OCR technology has become a business necessity helping businesses to transition towards digitalization by capturing, evaluating, and maintaining sensitive data and holding its promise of monitoring efficient workflow across various sectors.

Recent Deep Learning advancements, such as the introduction of transformer topologies, have helped us accelerate our handwritten character recognition. Intelligent Character Recognition (ICR), is a term used to describe the process for recognizing handwritten content. ICR algorithms require more intelligence than ordinary OCR. This post will cover the challenges of handwritten text identification and the techniques that can be used to tackle them using deep learning and machine learning. In the healthcare/pharmaceutical industry, patient medication digitization is a serious issue. Roche processes millions of PDFs each day, processing petabytes in medical PDFs.

Character recognition is a process that allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that computers can use. As a part of this case study, we are going to recognize "Hindi characters". It is a Character Recognition problem related to computer vision, where our task is to predict the Hindi character present in the image. The Model should predict or recognize the character present in the image in real-time. So the latency of the model should be low.

In this paper, we introduce a model-based omnifont Persian OCR system. The system uses a set of 8 primitive elements as structural features for recognition. First, the scanned document is preprocessed. After normalizing the preprocessed image, text rows and sub-words are separated and then thinned. After recognition of dots in sub-words, strokes are extracted and primitive elements of each sub-word are recognized using the strokes. Finally, the primitives are compared with a predefined set of character identification vectors in order to identify sub-word characters. The separation and recognition steps of the system are concurrent, eliminating unavoidable errors of independent separation of letters. The system has been tested on documents with 14 standard Persian fonts in 6 sizes. The achieved precision is 97.06%.

Plate.Vision is a vehicle identification software through license plate recognition,

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