Search Results for "tiny-core-plus" - Page 2
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Showing 36 open source projects for "tiny-core-plus"
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Classnotes is an OCR intended to translate handwritten scans into text. In order for the program to translate the scans the user must create a handwriting profile by training the OCR with scans.
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Scanner/Touchscreen Input Handwriting Recognition Software: A handwriting recognition program for Windows that is able to recognize both images from a scanner and data from a touch screen. It also works as an OCR program.
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Waygoer is an OCR program. It is based on contour extraction and momentum transformations. This allows for rotation- and scale-invariant recognition. Waygoer is still at an early stage of development.
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A Neural network based, handwriting recognition software who's aim is to have a cursive OCR software. Although it is used in handwriting recognition, it can be used as well for creating Neural Networks and learning of those networks.
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An omnifont OCR software for KDE. Due to the fact that each step of the OCR process can be visualized you can get a quick idea of how OCR works and where the problems lie. However the program may be of minor/no use for end users in its current state.
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XWA Scores is an OCR dedicated to the game "XWing Alliance". It's main goal is to convert the scores from a screen capture.
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When translating becomes a game ! Text to translate can be graphically selected. Several dictionnaries can be sorted according to the context. A large choice of matching strategies is available. The OCR engine is tunable.
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OCR Reader
The tool supports template-based parsing, allowing structured output i
OCR Reader is a lightweight Windows utility designed to extract text from PDF files and images using OCR (Tesseract engine). The tool supports template-based parsing, allowing structured output into CSV or TXT without manual coding. Core components Tesseract OCR engine Poppler (PDF rendering) Template-based extraction system Homepage: https://martan1484.github.io/OCR_Reader -
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It's a dumb, but lightning fast OCR engine, not using any fancy math to recognize text from screenshots. Aim to recognize 100% of text from screenshot, even if image is affected by jpeg compression or text is affected by antialiasing.
