552 skills found · Page 1 of 19
clovaai / Deep Text Recognition BenchmarkText recognition (optical character recognition) with deep learning methods, ICCV 2019
otiai10 / GosseractGo package for OCR (Optical Character Recognition), by using Tesseract C++ library
kha-white / Manga OcrOptical character recognition for Japanese text, with the main focus being Japanese manga
hwalsuklee / Awesome Deep Text Detection RecognitionA curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.
rmtheis / Android OcrExperimental optical character recognition app
GauravSingh9356 / J.A.R.V.I.SPersonal Assistant built using python libraries. It does almost anything which includes sending emails, Optical Text Recognition, Dynamic News Reporting at any time with API integration, Todo list generator, Opens any website with just a voice command, Plays Music, Wikipedia searching, Dictionary with Intelligent Sensing i.e. auto spell checking, Weather Reporting i.e. temp, wind speed, humidity, YouTube searching, Google Map searching, Youtube Downloading, etc.
kangoka / Tiktodv3TIKTOD V3 is a bot application designed to automate interactions on Zefoy website, such as increasing views, hearts, followers, and shares on a specified video. The bot uses technologies like Selenium for web automation and OCR (Optical Character Recognition) for solving captchas.
OCR4all / OCR4allProvides OCR (Optical Character Recognition) services through web applications
evilgix / EvilOptical Character Recognition in Swift for iOS&macOS. 银行卡、身份证、门牌号光学识别
aashrafh / MozartAn optical music recognition (OMR) system. Converts sheet music to a machine-readable version.
kdzwinel / JS OCR DemoJavaScript optical character recognition demo
ZumingHuang / Awesome Ocr ResourcesA collection of resources (including the papers and datasets) of OCR (Optical Character Recognition).
blueaxis / PoricomOptical character recognition in manga images. Manga OCR desktop application
apacha / OMR DatasetsCollection of datasets used for Optical Music Recognition
tensorflow / MoonlightOptical music recognition in TensorFlow
puhuilab / Phocran open high-performance Optical Character Recognition (OCR) toolkit
dhvanikotak / Emotion Detection In VideosThe aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
cseas / Ocr TableExtract tables from scanned image PDFs using Optical Character Recognition.
rsommerfeld / TrocrPowerful handwritten text recognition. A simple-to-use, unofficial implementation of the paper "TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models".
dwqs / Ollama OcrA powerful OCR (Optical Character Recognition) package that uses state-of-the-art vision language models
