tten characters and numerals as well as special character, each sized 24x24 pixels. This dataset is designed to bolster Optical Character Recognition (OCR) research and development. 

 

For consistency, images extracted from the MNIST dataset have been color-inverted to match the grayscale aesthetics of the AlphaNum dataset. 

 

## Data Sources 

ting Recognition (ICFHR 2012), Bari, Italy, 2012, pp. 447-452, IEEE Computer Society. 

 

tten Characters  

 

## Dataset Description 

 

- **Model**: https://huggingface.co/iarata/Few-Shot-PHCR 

mese Optical Character Recognition - Visual Question Answering 

===== 

 

 

 

s of Optical Character Recognition (OCR) and Natural Language Processing (NLP), integrating multilingual capabilities remains a critical challenge, especially when considering languages with complex scripts such as Arabic. This paper introduces the Comprehensive Post-OCR Parsing and Receipt Understanding Dataset (CORU), a novel dataset specifically designed to enhance OCR and information extraction from receipts in multilingual contexts involving Arabic and English. CORU consists of over 20,000 annotated receipts from diverse retail settings in Egypt, including supermarkets and clothing stores, alongside 30,000 annotated images for OCR that were utilized to recognize each detected line, and 10,000 items annotated for detailed information extraction. These annotations capture essential details such as merchant names, item descriptions, total prices, receipt numbers, and dates. They are structured to support three primary computational tasks: object detection, OCR, and information extraction. We establish the baseline performance for a range of models on CORU to evaluate the effectiveness of traditional methods, like Tesseract OCR, and more advanced neural network-based approaches. These baselines are crucial for processing the complex and noisy document layouts typical of real-world receipts and for advancing the state of automated multilingual document processing. 

## Dataset Overview 

 

CORU is divided into Three challenges: 

 

ting Optical Character Recognition (OCR) models across various domains. It includes images and textual data derived from various open-source websites. 

This dataset aims to provide a comprehensive evaluation resource for researchers and developers working on OCR systems, particularly in Thai language processing. 

 

### Data Fields 

 

ough optical character recognition (OCR) technology, which extracts text from images, in this case, **the train number**. 

 

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets/train-numbers?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-trains-dataset)** to buy the dataset 

 

The dataset be used to train machine learning models for extracting and analyzing text from train-related documents or images, to develop algorithms or models for real-time updates, or building intelligent systems related to trains and transportation. 

ting Optical Character Recognition (OCR) models across various domains. It includes images and textual data derived from various open-source websites. 

This dataset aims to provide a comprehensive evaluation resource for researchers and developers working on OCR systems, particularly in Thai language processing. 

 

### Data Fields 

 

4045 character 

images, which is split into Thai handwritten character dataset (THI-C68) for 

14490 images and Thai handwritten digit dataset (THI-D10) for 9555 images. The 

data was collected from 150 native writers aged from 20 to 23 years old. The 

participants were allowed to write only the isolated Thai script on the form and 

- [Optical Character Recognition](#optical-character-recognition) 

- [OCR word confidence](#ocr-word-confidence) 

- [Dataset Structure](#dataset-structure) 

- [Data Instances](#data-instances) 

- [Data Fields](#data-fields) 

OCR (Optical Character Recognition) abilities of the [Turkish-LLaVA-v0.1](https://huggingface.co/ytu-ce-cosmos/Turkish-LLaVA-v0.1) model. It was created by collecting **100,000** books entirely from Turkish sources. The primary goal of this dataset is to enhance the model's ability to detect and interpret any text present in images. 

 

## Dataset Usage in Finetuning 

 

This dataset played a crucial role in the finetuning process of the Turkish-LLaVA-v0.1 model. It was concatenated with [another dataset](#) (Soon..) to form a comprehensive training set that significantly refined the model's OCR capabilities. This enhancement process ensured that the model could accurately recognize and interpret Turkish text in various visual contexts. 

y on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset. 

 

This dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative). 

to **Optical Character Recognition (OCR)** and is useful for retail.  

 

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets/ocr-receipts-text-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-receipts-text-detection)** to buy the dataset 

 

Each image in the dataset is accompanied by bounding box annotations, indicating the precise locations of specific text segments on the receipts. The text segments are categorized into four classes: **item, store, date_time and total**. 

lly, Optical Character Recognition (**OCR**) has been performed on each bounding box to extract the barcode numbers. 

 

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-barcodes-detection)** to buy the dataset 

 

The dataset is particularly valuable for applications in *grocery retail, inventory management, supply chain optimization, and automated checkout systems*. It serves as a valuable resource for researchers, developers, and businesses working on barcode-related projects in the retail and logistics domains. 

OCR (Optical Character Recognition) recognition, you can explore various open-source platforms and repositories such as GitHub, Model Zoo, or specific frameworks' model hubs like TensorFlow Hub or PyTorch Hub. ID OCR recognition models are designed to extract text from identity cards, including personal details like name, ID number, date of birth, and other relevant information. These models are trained on diverse datasets to accurately recognize and extract text from various ID card formats and designs.  

 

 

<a href="https://github.com/CCCpan/Gebaini"> Click on me free access </a> 

 

from Optical Character Recognition software on digitised newspaper pages. This dataset includes the plain text from the OCR alongside some minimal metadata associated with the newspaper from which the text is derived. 

 

This dataset can be used for: 

 

historical research and digital humanities research 

nd **Optical Character Recognition (OCR)** results. 

 

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets/ocr-machine-readable-zone-mrz?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-generated-machine-readable-zone-mrz-text-detection)** to buy the dataset 

 

This dataset is useful for developing applications related to *document verification, identity authentication, or automated data extraction from identification documents*. 

ayin characters, Latin 

characters, and 4 character symbols of Baybayin diacritics in MATLAB format. It 

consisted of 17000 images for Baybayin (1000 per character), 18200 images for 

Latin (700 per character), and 2000 images for Baybayin diacritics (500 per 

symbol). Each character image is strictly center-fitted with a size 56x56 

ates Optical Character Recognition (OCR) data during the pre-training phase of VLMs. This integration enhances vision-language alignment by providing detailed textual information alongside visual data. 

- **Text-Rich Content**: The dataset focuses on text-rich images and includes OCR-specific annotations, enabling more accurate handling of content like documents, charts, and other text-heavy visuals. 

- **Parquet Format**: The dataset is stored in Parquet format, facilitating efficient storage, processing, and retrieval of OCR metadata and images. This format is well-suited for handling large-scale datasets and can be easily integrated into data processing pipelines. 

 

### Purpose: