Search Results for "handwriting recognition source code" - Page 7
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Showing 165 open source projects for "handwriting recognition source code"
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Qt Handwriting Recognizing it's a simple Qt GUI interface of a artificial neural network to provide handwrite recognition. This project use FANN (Fast Artificial Neural Network) on first approach.
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Jarnal is a plattform-indepenent programm written in Java for notetaking, sketching and PDF-/picture-annotating. It works like a normal notepad with the difference of being digital. It is thought to be used with a stylus on tablet-pcs.
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Active Shape/Appearance Model Library (ASMLibrary/AAMLibrary) source code, which includes ASMBuilding/AAMBuilding as well as ASMFitting/AAMFitting algorithm. It is developped under OpenCV 1.0 for locating features in a face and face recognition.
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This is going to be a free open source handwriting recognition for persian,and also arabic,but not a bad one now!!
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Augmenting other natural interfaces namely handwriting, speech recognition to input Indian language characters to the computer. Speech synthesis is also provided to read out local language text.
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JEquation provides an intuitive and fast way to input mathematical expressions into latex documents by using prestructured neural network based handwriting recognition.
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phpOCR is an Optical Character Recognition system written in PHP. It can be used in automated scripts as well as web interface. Works best for small images like numeric SMS/email authorization codes, bar-code numbers and others.
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The Handwrite project provides access to and information on various handwriting recognition software.
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This Bash shell script automatically includes pieces of text into a major text file, through the recognition of start/end tag pairs.
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wav2vec2-large-xlsr-53-portuguese
Portuguese ASR model fine-tuned on XLSR-53 for 16kHz audio input
wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER). It achieves a WER of 11.3% (or 9.01% with LM) on Common...
