Text Extraction Using OpenCV Machine Learning They're also handy for quickly creating training datasets. A complex technique that can't improve on the metrics of these techniques is not worth implementing. People use them because they are simple, easy to implement, run fast on any hardware, and set a reference baseline for success metrics. However, in practice, they don't generalize very well, have lower recall than Tesseract, and often require manual adjustments to parameters. These steps are tested and adjusted while visually examining a representative set of input images in hopes that they'll work well for any unseen image. For any operation that takes parameters, manually adjust their values or ranges till the desired outcomes are visible.Use more fine-grained algorithms - like template matching or image descriptors - to isolate the text more precisely or to recognize the text characters.Use preprocessing operations - like converting image format, thresholding, erosion, dilation, color masking, colorspace conversions, or gaussian blurring - to isolate the text regions we're interested in.Manually observe patterns in the layouts, edges, colors, textures, font sizes, contours, morphologies (shapes), and any other features that help differentiate text areas from non-text regions.Alternative Image Processing TechniquesĪn alternative approach to Tesseract is to use traditional text processing techniques that typically involve the following stages: However, we still use Tesseract for prototyping as it provides a good baseline to judge the success metrics of other techniques. That's why we use more accurate alternatives in production. While it's easy to use, its simplicity comes at the cost of accuracy. Tesseract invariably requires heavy post-processing pipelines to improve its results. Even simple words are misrecognized and broken up into meaningless fragments. It's practically incapable of recognizing handwritten text. It’s frequently unable to recognize clear printed characters that are easily recognized by people. It shows low recall (i.e., high rate of missed detections) and high character error rates (CER). Unfortunately, as the image above depicts, we find Tesseract too unreliable and inaccurate for any production use cases. Older versions of Tesseract used a combination of image processing and statistical models, but the latest versions use deep learning algorithms. It consists of the tesseract-ocr engine and language-specific wrappers like pytesseract for Python. Python Tesseract-ocr recognition on a legal document - missed words, spelling mistakes, and handwritten text ignored ( Source ) In contrast, dense text refers to text in images where text is the primary content and the focus, such as text in books, invoices, and documents. Scene text refers to text that's incidentally present in a photo, such as text on product labels, billboards, traffic signs, vehicles, and so on. Text extraction often refers to the overall question of how to extract text using all three subtasks - detection, recognition, and information extraction. Information extraction refers to understanding the semantics and purpose of a piece of text. Text recognition refers to recognizing higher-level entities like characters, words, sentences, paragraphs, language, and other concepts of text organization using any kind of real-world knowledge such as language models and document layouts. Optical character recognition (OCR) refers to identifying characters using only the pixels in an image. Text detection refers to estimating which pixels in an image belong to text content. Let's start exploring how we have implemented our text extraction pipeline, starting with some basic concepts you should know for a foundational understanding. For new customer data, we just need a few dozen documents - regardless of file format - to fine-tune our system and have it produce accurate results. That's because our system can generalize well but, at the same time, is also flexible and customizable. We use the same text extraction system for all three use cases, though they seem so different. Our system can accurately extract text information from medical records, patient forms, prescriptions, handwritten opinions, medical imagery, and more. Medical Document Transcription & AutomationĪccurate transcription of medical documents is necessary to deliver high quality of healthcare, avoid legal liabilities, and resolve insurance problems smoothly. They can capture and extract product labels, bar codes, and other information that's critical for both back-office and storefront management in the retail and e-commerce industry. We have automated warehouse workflows and improved storefront operations by deploying our text extraction system for our retail and e-commerce customers.
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