Optical character recognition (OCR) API allows for application developer to extract text in the specific language from an image. Note: This sample is part of a large collection of UWP …

This repo collects OCR-related datasets. In general, the datasets are classified by 6 types, i.e., Natural Scene Text, Document Text, Handwritten Text, Historical Document Text, Video Text, …

ABBYY Cloud OCR SDK Code samples - Code samples for using the proprietary commercial ABBYY OCR API. OCR4all - Provides OCR services through web applications. Included …

2024年11月30日 · The Read OCR model extracts print and handwritten style text as lines and words. The model outputs bounding polygon coordinates and confidence for the extracted words.

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

This repository contains sample files for common OCR file formats in a standardized directory layout to make it easier for tools to test. This is a sister project to ocr-fileformat which contains code for format validation and inter-format transformation. Samples are placed in ./samples/<format>/<format-version>/<sample>.

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars ...

5 天之前 · Optical Character Recognition (OCR) The Vision API can detect and extract text from images. There are two annotation features that support optical character recognition (OCR): TEXT_DETECTION...

2024年11月12日 · Train Machine Learning Models Faster with 15 Best Open-source Handwriting & OCR Datasets. Because of free data availability, the cost of developing the application is reduced significantly.

Each video is annotated with frame indexes to facilitate consistent and reproducible OCR benchmarks. Currently, the dataset includes over 25 curated videos, yielding thousands of extracted frames that present a variety of text-related challenges.