Languages support: At the time of writing, Google Vision fully supports 60 languages.There is usually no need to develop and train your own model. User-friendliness: Once the setup is completed, Google Vision is easy to use.Moreover, Google Vision offers other functionalities such as object detection in images and OCR for handwritten documents/images. Versatility: The tool performs well across a wide range of documents.It is among the best ‘out-of-the-box’ tools when it comes to recognising characters. Character detection accuracy: Although it has its limitations, Google Vision tends to be highly accurate, even in cases where other tools might struggle, such as when several languages coexist in the same text.The Pros and Cons of Google Vision, Tesseract, and their Powers Combined Google Vision The principle of exploring different combinations of tools to create customised workflows is widely applicable in digital humanities projects, where tools tailored to our data are not always available. Combining both tools creates a “one-size-fits-most” method that will generate high-quality OCR outputs for a wide range of documents. Google Cloud Vision is one of the best ‘out-of-the-box’ tools when it comes to recognising individual characters but, contrary to Tesseract, it has poor layout recognition capabilities. This lesson offers a possible alternative by introducing two ways of combining Google Vision’s character recognition with Tesseract’s layout detection. Fortunately, tools such as Tesseract, TRANSKRIBUS, OCR4all, eScriptorium and OCR-D (among others) have allowed humanities scholars to work with all kinds of documents, from handwritten nineteenth-century letters to medieval manuscripts.ĭespite these great tools, it can still be difficult to find an OCR solution that aligns with our technical knowledge, can be easily integrated within a workflow, or can be applied to a multilingual/diverse corpus without requiring any extra input from the user. However, OCR becomes trickier when dealing with historical fonts and characters, damaged manuscripts or low-quality scans. Whether you are interested in network analysis, named entity recognition, corpus linguistics, text reuse, or any other type of text-based analysis, good quality Optical Character Recognition (OCR), which transforms a PDF to a computer-readable file, will be the first step. Historians working with digital methods and text-based material are often confronted with PDF files that need to be converted to plain text. Combining Layout and Character Recognition.The Pros and Cons of Google Vision, Tesseract, and their Powers Combined.
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