#ocrtraining

[ocrkings]

Spartan Race Trifecta Winner 💪 #spartanrace #ocrtraining

[ocrkings]

Fitness Journey Spartan Race #fitnessjourney #spartan

[ocrkings]

Spartan Race Night Run #spartan #winner

[globosetechnologysolutionss]

A Comprehensive List of OCR Datasets for Machine Learning

Introduction:

Optical Character Recognition (OCR) is a game-changing technology that allows computers to interpret and convert various types of documents, images, and handwritten text into editable and machine-readable formats. OCR has revolutionised data extraction, document digitization, and information retrieval processes across industries. To build accurate and robust OCR models, access to high-quality training data is crucial. In this blog, we present a comprehensive list of OCR datasets that are invaluable resources for training OCR machine learning models.

MNIST (Modified National Institute of Standards and Technology):

The MNIST dataset is one of the most widely used benchmarks in OCR research. It consists of 28x28 grayscale images of handwritten digits (0 to 9) and their corresponding labels. While primarily used for digit recognition, MNIST serves as an excellent starting point for OCR beginners due to its simplicity and accessibility.

IAM Handwriting Database:

This dataset focuses on handwritten English text recognition. It contains more complex and varied text samples compared to MNIST. The IAM Handwriting Database includes text lines written by different individuals, allowing OCR models to learn diverse handwriting styles and variations.

Street View Text (SVT) Dataset:

The SVT dataset is designed for scene text recognition, simulating real-world scenarios where text is captured in natural environments like street signs or storefronts. The dataset contains images of scene text along with corresponding annotations, providing a challenging and practical OCR training resource.

IIIT 5K-Words Dataset:

Similar to SVT, the IIIT 5K-Words Dataset focuses on scene text recognition. It consists of images collected from the web, capturing text in various languages and fonts. This dataset offers a broader scope for OCR models to handle multilingual and diverse textual content.

CORD Dataset:

The CORD dataset caters to OCR needs in the medical domain. It comprises a collection of scientific papers related to COVID-19, enabling the training of OCR models to extract valuable information from research documents.

CAPTCHA Images:

CAPTCHA images, designed to prevent automated bots from accessing websites, can serve as interesting OCR training data. Though challenging due to image distortions and obfuscations, using CAPTCHA images can help OCR models improve their robustness and accuracy.

Tobacco3482:

The Tobacco3482 dataset is specifically tailored for OCR in historical documents. It contains images of tobacco advertisements from the early 20th century, offering unique challenges in recognising older fonts and styles.

UNLV-ISRI-ALPR Dataset:

This dataset focuses on Automatic License Plate Recognition (ALPR). It includes images of licence plates with annotations, enabling OCR models to recognise alphanumeric characters present on licence plates accurately.

Conclusion:

As a leading technology solution provider, Globose Technology Solutions Pvt Ltd (GTS) recognizes that OCR datasets are the bedrock of successful OCR models. These datasets empower researchers and practitioners to push the boundaries of text recognition technology. With our commitment to cutting-edge solutions and a dedication to advancing OCR research, GTS stands as your partner in harnessing the power of OCR datasets for building accurate and innovative OCR solutions.

[jmdain]

So, last week I fell two workouts short of my plan, and got ZERO writing done. So, this week I've made modifications. Removed my evening casual mileage two nights a week so i can use those days to focus on book 2. My Monday-Friday lunch gym time is still in effect.Exercise 1 of 8 for the week complete. Leg day! Today's exercises were the same as last Monday, except 40sec sets instead of 16 reps per set. I used 8# weights for all...which is up from last week's 0-5#. My knees are still not happy, so I didnt squat/lunge very deep at all. And overall, the workout was quick. Bonus! I also ended up doing the workout in my home gym, because I forgot my gym clothes. 🤦‍♀️

[jmdain]

Incline training for gym round 1 today. #WarriorInTraining #15kTraining #OCRTraining #DraelinInTraining #TheVaydeChronicles #Writer (at Hard Core Fitness) https://www.instagram.com/p/CqDosOUOiHQ/?igshid=NGJjMDIxMWI=

[freddieboy]

Yoohoo!!!! It's Friday-Eve! 💪 HIIT on a Thursday💪 #Spartan #SpartanRacePH #OCR #OCRTraining (at FitCamp Cebu) https://www.instagram.com/p/CfJclpKpWjE/?igshid=NGJjMDIxMWI=

[freddieboy]

VMSFCA Mass Workout with Coach Richard! Thank you coach for the running drills and workout in prep for #SpartanRacePH Cebu #OCRTraining #Spartan #SpartanRace #OCR (at Abellana Sports Complex Oval. Layshoo Kaayo Ang Venue) https://www.instagram.com/p/Ce8q3nEpDNH/?igshid=NGJjMDIxMWI=