Optical Character Recognition (OCR)
Discover an OCR software that simply works better.
Industry-Best OCR Technologies
Grooper uses the latest AI technology and our patented OCR methods to give you near-perfect text. Whether it’s paper or electronic documents, Grooper provides the best OCR and data capture.
Image Processing + OCR
OCR alone is far too inaccurate. So Grooper improves OCR accuracy by first removing lines, non-text objects, cleaning up document edges and much more.
Ensured Accuracy with Grooper’s Patented OCR
Grooper’s OCR software holds two patents from the United States Patent and Trademark Office to ensure superior accuracy.
- Flexible, dynamic OCR based data extraction – Patent #10,740,638
- OCR systems and methods – Patent #10,679,089
In a lab test, Grooper accurately captured 99.91% of text. Using OCR alone was half as accurate.
Cheat Sheet: How to Select the Right OCR Software
There are many things that make some OCR software better than others.
In this free Cheat Sheet, you will discover the most important qualities to look for in the best OCR software, such as:
- How some OCR software uses letter matching to get more accurate recognition results
- 3 Vital document imaging technologies used by the best OCR platforms
- Which 15 image file processing features boost OCR from average to excellent
- Why a slight increase in data accuracy can eliminate countless hours of manual data entry
AI-Enhanced OCR
Documents with many fonts make character recognition tricky. For example, checks include many fonts and handwriting. Grooper can extract all data in one pass by combining several AI engines with traditional OCR.
Iterative OCR – Capture Missed Text
This captures text missed on the first pass. To get the rest of the text, Grooper runs OCR several times.
Recognized text is then dropped out of the document image. As each OCR pass has fewer distractions, character recognition improves.
Bound Region Detection
This method targets text bound on all sides by lines.
Extracted text from each box is removed before full-page OCR. Because the location of the text boxes is understood, all text is intelligently joined back together.
Segment Reprocessing
Grooper understands the layout of text on a page, so groups of data are viewed as segments. Grooper re-runs recognition on low accuracy segments of text until it gets the best accuracy.
Cellular Validation – Capture Columns of Text
Multiple columns are tough for OCR, especially when columns of text are offset, have different fonts, or font sizes.
This causes big problems for standard OCR. However, Grooper splits an image into a grid, so each area is processed independently for top OCR accuracy.
Font Pitch Detection
Different document fonts present a big challenge for OCR. Fonts have different amounts of space between characters on documents. OCR has trouble with this and captures data incorrectly.
But Grooper’s font pitch detection looks at the width of each character and the space around them to see the correct way to capture data.
Intelligent Spell Correction
Grooper artificial intelligence corrects many OCR mistakes. It uses tools like K-Means Clustering, text removal, and text correction engines. What errors does Grooper correct?
- Simple document capture mistakes in data strings that do not match words in a dictionary
- Human-generated typos
- Inserting spaces where OCR falsely jammed words together
- Deleting strings of data that are not numbers or letters, such as censorship, like “$#@*! ”
- Repairing numbers (like prices) where aggressive image cleanup mistakenly removed punctuation
How Grooper OCRs PDF Documents
There is no standard for generating a PDF, so capturing text off a PDF has varying levels of difficulty:
- Some PDFs are purely text-based (easy to capture from as they contain machine-readable text)
- Others are document scans in PDF format (difficult)
- Others have combinations of the two types scattered through pages (most difficult)
How Grooper Gets Text from PDFs:
Grooper looks at each PDF page and places it in a category: image-based, text-based, or mixed-content. Then each page is handled accordingly:
- For PDF pages that have one image covering the entire page, process the page as an image-based page
- For text document pages that contain no images, extract only the raw text
- For mixed-content pages, extract each image to a temporary image, process the image, and merge the results with the raw text
Additional OCR Tools
Trainable OCR
Grooper OCR is trainable. The engine supports training custom and difficult font formats.
Performance Balancing
Grooper’s “Run Speed” option provides control to achieve balance between accuracy and performance. Learn how to speed up your OCR here.
Language Support
Grooper recognizes 268 distinct languages and 523 regional cultures. Language detection interprets dates, times, currency names, numeric formats, and more.
Electronic Text
Grooper avoids OCR altogether when dealing with original text-based file formats like Word, Excel, and Text PDFs. Instead, Grooper pulls complete and perfect text directly from the file.
Watch: Add a Chatbot to Your OCR for Maximum Data Recognition
Use the latest AI to get OCR results that are a cut above. We will show you how to use chatbots like ChatGPT with your OCR software to extract text from images and improve the data from your business documents.
Discover 4 new technologies:
- How to use AI OCR to improve poorly scanned documents
- How to use the latest and best OCR engines without the cloud.
- Talk with your documents with LLMs to get data that was never before possible.
- How to quickly create the most efficient data workflows
Optical Character Recognition (OCR) FAQs
What is OCR?
OCR, or Optical Character Recognition, is technology that converts text from scanned documents, images, and image-based PDFs into machine-readable text. This allows document software to search, classify, extract, and process information that would otherwise exist only as pixels on a page. In Grooper, OCR is performed through the Recognize activity, which obtains machine-readable text from scanned pages and imported image-based content.
How does OCR work in Grooper?
In Grooper, OCR works by using an OCR Profile during the Recognize activity. The OCR Profile controls which OCR engine is used, whether image cleanup is applied before OCR, how OCR Synthesis is configured, and how OCR results are filtered or reprocessed. This lets organizations tune OCR behavior for different document types, image qualities, and extraction needs instead of relying on one generic OCR setting.
What makes Grooper OCR different from basic OCR software?
Grooper OCR is different from basic OCR software because it is part of a larger intelligent document processing platform. Basic OCR converts images into text, but Grooper uses OCR text to support document classification, data extraction, validation, and automation. Grooper also gives users fine-grained control over OCR engine selection, pre-OCR image cleanup, OCR Synthesis, result filtering, and reprocessing.
Can Grooper use multiple OCR engines?
Yes. Grooper supports multiple OCR engines and allows organizations to match their recognition strategy to specific document types rather than forcing a one-size-fits-all engine. Within an OCR Profile, administrators can configure and tune multiple enterprise engines based on data residency and volume needs:
- Azure DI OCR: Integrates cloud-based Microsoft Azure Document Intelligence via API keys to leverage cloud text recognition.
- Transym OCR 5: An on-premises engine designed for high-volume machine print where data residency requirements prohibit cloud transmission.
- Tesseract OCR: An adaptable open-source option embedded inside the platform that can be trained on custom or difficult font formats.
How does Grooper achieve its 99.91% text extraction accuracy rate?
Grooper’s documented 99.91% lab accuracy rate—double the performance of traditional baseline OCR alone—is backed by two distinct United States patents (#10,740,638 and #10,679,089) for flexible, dynamic data extraction. The platform uses targeted structural features to fix text anomalies:
- Iterative OCR: The system reads the page, targets what was read poorly, drops out the text it successfully captured with high confidence, and re-processes the remaining noisy areas without visual distractions.
- Segment Reprocessing: Isolates small blocks or lines of text on a document that fall below confidence thresholds and runs dedicated second-pass OCR algorithms directly on those exact coordinates.
- Cellular Validation: Divides document blocks into a customized grid of rows and columns, enabling the system to evaluate separate vertical and horizontal sections of a document independently.
- Intelligent Spell Correction: Applies K-Means Clustering and native correction dictionaries to repair broken price punctuation, remove human typos, and separate falsely joined words.
What is OCR Synthesis in Grooper?
OCR Synthesis is the structural architecture that assembles recognized text into a coherent digital document context. Instead of returning a disjointed string of characters, OCR Synthesis ingests spatial coordinates along with layout data harvested during preprocessing.
The engine uses advanced font awareness to re-analyze horizontal spaces, tabs, and new-line feeds. It then synthesizes data from specialized capture tools—such as Bounded Region Detection, Segment Reprocessing, Iterative OCR, Zonal OCR and Cellular Validation—re-weaving them into one logical, structured text flow.