OCR Text Detection in the Documents Object Detection dataset

The dataset is a collection of images that have been annotated with the location of text in the document. The dataset is specifically curated for text detection and recognition tasks in documents such as scanned papers, forms, invoices, and handwritten notes.

The dataset contains a variety of document types, including different layouts, font sizes, and styles. The images come from diverse sources, ensuring a representative collection of document styles and quality. Each image in the dataset is accompanied by bounding box annotations that outline the exact location of the text within the document.

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The Text Detection in the Documents dataset provides an invaluable resource for developing and testing algorithms for text extraction, recognition, and analysis. It enables researchers to explore and innovate in various applications, including optical character recognition (OCR), information extraction, and document understanding.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F6986071a88d8a9829fee98d5b49d9ff8%2FMacBook%20Air%20-%201%20(1).png?generation=1691059158337136&alt=media" alt="">

Dataset structure

• images - contains of original images of documents
• boxes - includes bounding box labeling for the original images
• annotations.xml - contains coordinates of the bounding boxes and labels, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes and labels for text detection. For each point, the x and y coordinates are provided.

Labels for the text:

• "Text Title" - corresponds to titles, the box is red
• "Text Paragraph" - corresponds to paragraphs of text, the box is blue
• "Table" - corresponds to the table, the box is green
• "Handwritten" - corresponds to handwritten text, the box is purple

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F38e02db515561a30e29faca9f5b176b0%2Fcarbon.png?generation=1691058761924879&alt=media" alt="">

Text Detection in the Documents might be made in accordance with your requirements.

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TrainingData provides high-quality data annotation tailored to your needs

keywords: text detection, text recognition, optical character recognition, document text recognition, document text detection, detecting text-lines, object detection, scanned documents, deep-text-recognition, text area detection, text extraction, images dataset, image-to-text

What’s Included

Introducing the English Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the English language.

Containing a total of 2000 images, this English OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible English text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native English people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the English language. Your journey to enhanced language understanding and processing starts here.

OCR_Datasets

## Overview

OCR_Datasets is a dataset for object detection tasks - it contains Words annotations for 498 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

OCR

## Overview

OCR is a dataset for vision language (multimodal) tasks - it contains TEXT annotations for 540 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Corpus for the ICDAR2019 Competition on Post-OCR Text Correction (October 2019)Christophe Rigaud, Antoine Doucet, Mickael Coustaty, Jean-Philippe Moreuxhttp://l3i.univ-larochelle.fr/ICDAR2019PostOCR-------------------------------------------------------------------------------These are the supplementary materials for the ICDAR 2019 paper ICDAR 2019 Competition on Post-OCR Text CorrectionPlease use the following citation:@inproceedings{rigaud2019pocr,title=""ICDAR 2019 Competition on Post-OCR Text Correction"",author={Rigaud, Christophe and Doucet, Antoine and Coustaty, Mickael and Moreux, Jean-Philippe},year={2019},booktitle={Proceedings of the 15th International Conference on Document Analysis and Recognition (2019)}}

Description: The corpus accounts for 22M OCRed characters along with the corresponding Gold Standard (GS). The documents come from different digital collections available, among others, at the National Library of France (BnF) and the British Library (BL). The corresponding GS comes both from BnF's internal projects and external initiatives such as Europeana Newspapers, IMPACT, Project Gutenberg, Perseus and Wikisource. Repartition of the dataset- ICDAR2019_Post_OCR_correction_training_18M.zip: 80% of the full dataset, provided to train participants' methods.- ICDAR2019_Post_OCR_correction_evaluation_4M: 20% of the full dataset used for the evaluation (with Gold Standard made publicly after the competition).- ICDAR2019_Post_OCR_correction_full_22M: full dataset made publicly available after the competition. Special case for Finnish language Material from the National Library of Finland (Finnish dataset FI > FI1) are not allowed to be re-shared on other website. Please follow these guidelines to get and format the data from the original website.1. Go to https://digi.kansalliskirjasto.fi/opendata/submit?set_language=en;2. Download OCR Ground Truth Pages (Finnish Fraktur) [v1](4.8GB) from Digitalia (2015-17) package;3. Convert the Excel file ""~/metadata/nlf_ocr_gt_tescomb5_2017.xlsx"" as Comma Separated Format (.csv) by using save as function in a spreadsheet software (e.g. Excel, Calc) and copy it into ""FI/FI1/HOWTO_get_data/input/"";4. Go to ""FI/FI1/HOWTO_get_data/"" and run ""script_1.py"" to generate the full ""FI1"" dataset in ""output/full/"";4. Run ""script_2.py"" to split the ""output/full/"" dataset into ""output/training/"" and ""output/evaluation/"" sub sets.At the end of the process, you should have a ""training"", ""evaluation"" and ""full"" folder with 1579528, 380817 and 1960345 characters respectively.

Licenses: free to use for non-commercial uses, according to sources in details- BG1: IMPACT - National Library of Bulgaria: CC BY NC ND- CZ1: IMPACT - National Library of the Czech Republic: CC BY NC SA- DE1: Front pages of Swiss newspaper NZZ: Creative Commons Attribution 4.0 International (https://zenodo.org/record/3333627)- DE2: IMPACT - German National Library: CC BY NC ND- DE3: GT4Hist-dta19 dataset: CC-BY-SA 4.0 (https://zenodo.org/record/1344132)- DE4: GT4Hist - EarlyModernLatin: CC-BY-SA 4.0 (https://zenodo.org/record/1344132)- DE5: GT4Hist - Kallimachos: CC-BY-SA 4.0 (https://zenodo.org/record/1344132)- DE6: GT4Hist - RefCorpus-ENHG-Incunabula: CC-BY-SA 4.0 (https://zenodo.org/record/1344132)- DE7: GT4Hist - RIDGES-Fraktur: CC-BY-SA 4.0 (https://zenodo.org/record/1344132)- EN1: IMPACT - British Library: CC BY NC SA 3.0- ES1: IMPACT - National Library of Spain: CC BY NC SA- FI1: National Library of Finland: no re-sharing allowed, follow the above section to get the data. (https://digi.kansalliskirjasto.fi/opendata)- FR1: HIMANIS Project: CC0 (https://www.himanis.org)- FR2: IMPACT - National Library of France: CC BY NC SA 3.0- FR3: RECEIPT dataset: CC0 (http://findit.univ-lr.fr)- NL1: IMPACT - National library of the Netherlands: CC BY- PL1: IMPACT - National Library of Poland: CC BY- SL1: IMPACT - Slovak National Library: CC BY NCText post-processing such as cleaning and alignment have been applied on the resources mentioned above, so that the Gold Standard and the OCRs provided are not necessarily identical to the originals.

Structure- **Content** [./lang_type/sub_folder/#.txt] - ""[OCR_toInput] "" => Raw OCRed text to be de-noised. - ""[OCR_aligned] "" => Aligned OCRed text. - ""[ GS_aligned] "" => Aligned Gold Standard text.The aligned OCRed/GS texts are provided for training and test purposes. The alignment was made at the character level using ""@"" symbols. ""#"" symbols correspond to the absence of GS either related to alignment uncertainties or related to unreadable characters in the source document. For a better view of the alignment, make sure to disable the ""word wrap"" option in your text editor.The Error Rate and the quality of the alignment vary according to the nature and the state of degradation of the source documents. Periodicals (mostly historical newspapers) for example, due to their complex layout and their original fonts have been reported to be especially challenging. In addition, it should be mentioned that the quality of Gold Standard also varies as the dataset aggregates resources from different projects that have their own annotation procedure, and obviously contains some errors.

ICDAR2019 competitionInformation related to the tasks, formats and the evaluation metrics are details on :https://sites.google.com/view/icdar2019-postcorrectionocr/evaluation

References - IMPACT, European Commission's 7th Framework Program, grant agreement 215064 - Uwe Springmann, Christian Reul, Stefanie Dipper, Johannes Baiter (2018). Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin. - https://digi.nationallibrary.fi , Wiipuri, 31.12.1904, Digital Collections of National Library of Finland- EU Horizon 2020 research and innovation programme grant agreement No 770299

Contact- christophe.rigaud(at)univ-lr.fr- antoine.doucet(at)univ-lr.fr- mickael.coustaty(at)univ-lr.fr- jean-philippe.moreux(at)bnf.frL3i - University of la Rochelle, http://l3i.univ-larochelle.frBnF - French National Library, http://www.bnf.fr

Standard Ocr Dataset 2

## Overview

Standard Ocr Dataset 2 is a dataset for object detection tasks - it contains Characters annotations for 206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for "OCR-VQA"

More Information needed

Ocr

## Overview

Ocr is a dataset for object detection tasks - it contains Digis annotations for 237 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

This dataset has been created by Stability AI and LAION. SynthText is a popular OCR dataset, where random texts are rendered into random locations in images based on depth maps. In this dataset, we additionally computed image captions using BLIP2.

Caption: "a close up of a leopard's face with a blurry background"

Unlock the potential of Tesseract OCR with our meticulously hand-labeled training dataset. Designed for fine-tuning, this dataset includes comprehensive files and a custom Bash script to streamline your OCR improvements.

This dataset consists of 156 pages of Romanian texts written in the Romanian Transitional Script (RTS). RTS is a mix of Latin and Cyrillic characters that were used in the 19th century in the Romanian provinces to facilitate the transition from the Romanian Cyrillic Script to the modern Latin Script. The images cover the period between 1833 and 1864. The selected texts cover a diverse range of literary genres, including poems, novels, dramas, stories, newspapers, and religious texts.

The dataset was obtained from the Central University Libraries (BCU) of Timișoara, Iași, and Cluj-Napoca through their free online platforms or by request. The scanned images are provided in JPEG and PNG formats, with dimensions ranging from approximately 300 by 900 pixels to 2000 by 3000 pixels. The file sizes vary between 70 KB and 10 MB.

To ensure diversity, the dataset includes images with various fonts, styles, regions, publishers, and years. It covers all three main Romanian provinces' key publishing regions (Bucharest - B, Iasi - IS, Brasov - BV, Sibiu - SB, Blaj - BJ) as well as some located outside Romania that printed texts in RTS (Vienna - V, Budapest - BD, Paris - P). It comprises 4588 lines of text, totaling 31,132 words and 158,656 characters. Among these characters, there are 61,065 Cyrillic characters, 27,022 Latin characters, 53,844 overlapping characters (identical symbols), and 16,725 other characters (e.g., punctuation, digits). The images below summarize its content per publisher and decade. More statistics (including per publishing house and per character) are available in the code provided.

Statistics of characters in the dataset per publisher and decade* https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15661653%2F13bd86216df169b5c4783813a4b5118f%2Fchar-count.png?generation=1687532923729343&alt=media" alt="">

Percentage of Latin vs. Cyrillic vs. other characters in the dataset* https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15661653%2F0cfad1574aa2823b798fcf2b515beff6%2Fchar-ratio.png?generation=1687532980067286&alt=media" alt="">

The dataset presents typical challenges found in old documents, such as wear and tear, blemishes, discolorations, library imprints, handwriting, ink smudges, and variations in text alignment. These factors may impact legibility, and some scanned lines of text may not be uniformly straight.

This dataset provides a valuable resource for researchers and practitioners interested in historical document analysis, transliteration techniques, and studying the evolution of the Romanian language. It allows for the development and evaluation of OCR models and other language processing techniques in the context of the Romanian Transitional Script. The images provided are accompanied by ground truth texts (.gt.txt files) containing the correct text found in them, as well as .box files for the Tesseract 5 OCR engine.

Usage

You may use the dataset freely as long as you mention this page or the project below.

Acknowledgements

This work was supported by a grant of the Romanian Ministry of Research, Innovation and Digitization, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2021-0693, within PNCDI III. Project website: ROTLA

*Plots are based on the original dataset distribution

This is a dataset comprising 813 images of invoices and receipts of a private company in the Portuguese language. It also includes text files with the transcription of relevant fields for each document – seller name, seller address, seller tax identification, buyer tax identification, invoice date, invoice total amount, invoice tax amount, and document reference.

OCR (Optical Character Recognition) barcode detection is a technology that enables the automatic recognition and extraction of barcode information from images or documents...

Ocr

## Overview

Ocr is a dataset for object detection tasks - it contains Meter Number annotations for 3,786 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

This dataset comprises a meticulously augmented collection of Gurmukhi handwritten characters, designed to enhance the performance of machine learning models in optical character recognition (OCR) and related tasks. It includes characters across 41 distinct classes, each augmented to reach a total of approximately 290 samples per class.

Key Features:

Gurmukhi Script Focus: The dataset exclusively features handwritten characters from the Gurmukhi script, catering specifically to applications involving Punjabi language processing. Diverse Augmentations: Images have been subjected to a range of transformations, including rotations, shifts, shears, zooms, and horizontal flips, promoting robustness to variations encountered in handwritten text. Consistent Dimensions: All images are resized to a uniform 256x256 resolution, ensuring compatibility with most deep learning architectures. Class-Specific Organization: Images are neatly organized into 41 folders, each representing a distinct Gurmukhi character, facilitating targeted training and evaluation. Handwritten Data Collection: The original images used for augmentation were collected from 10 volunteers, introducing natural variability in writing styles and further enhancing the dataset's diversity. Potential Use Cases:

Gurmukhi OCR: Train and evaluate OCR models specifically for Gurmukhi script recognition. Handwriting Recognition: Develop models capable of recognizing and transcribing handwritten Gurmukhi text. Script Style Analysis: Explore the variations in handwriting styles within the Gurmukhi script.

A Fire and Smoke Dataset is a collection of images and data specifically curated for the development, training, and evaluation of machine learning models and computer vision algorithms designed to detect and classify fires and smoke in various environments..

This dataset we prepared using the Scanned receipts OCR and information extraction(SROIE) dataset. The SROIE dataset contains 973 scanned receipts in English language. Cropping the bounding boxes from each of the receipts to generate this text-recognition dataset resulted in 33626 images for train set and 18704 images for the test set. The text annotations for all the images inside a split are stored in a metadata.jsonl file. usage: from dataset import load_dataset data =… See the full description on the dataset page: https://huggingface.co/datasets/priyank-m/SROIE_2019_text_recognition.

New Data OCR

## Overview

New Data OCR is a dataset for object detection tasks - it contains Inscriptions annotations for 580 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

atc96/OCR-VQA-200K dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

The Aida Calculus Math Handwriting Recognition Dataset consists of 100,000 images in 10 batches. Each image contains a photo of a handwritten calculus math expression (specifically within the topic of limits) written with a dark utensil on plain paper. Each image is accompanied by ground truth math expression in LaTeX as well as bounding boxes and pixel-level masks per character. All images are synthetically generated.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5602706%2F67bf0c680286baf2c979c8207a991bb2%2FScreen%20Shot%202020-08-19%20at%201.02.50%20PM.png?generation=1597868629120369&alt=media%20=500x100" alt="">

Motivation

The complexity of handwriting recognition for math expressions can be decomposed into the following sources of variability:

Image of Math = Math Expression x Math Characters x Location of Math Characters x Visual Qualities of the Math Characters (fonts, color) x Noise of Image (backgrounds, stray marks)

It is the job of the recognition model to take the Image of Math as input and predict the Math Expression.
Typical approaches to handwritten recognition tasks involve collecting and tagging of large amounts of data, on which many iterations of models are trained. The "one dataset, many models" paradigm has specific drawbacks within the context of product development. As product requirements evolve, such as the addition of a new mathematical character into the prediction space, a new data collection and tagging effort must be undertaken. The cycle of adapting the handwriting recognition capability to new requirements is long and does not support agile product development.

Here, we take a different approach by iteratively building a complex, synthetically generated dataset towards specific requirements. The generation process delivers exact control over the distribution of math expressions, characters, location of characters, specific visual qualities of the math, image noise, and image augmentations to the developer. The developer controls every aspect of the data, down to each pixel. In many ways, the data synthesis runs backwards to the handwriting recognition model, creating visual complexity that the model must then untangle to uncover the ground truth math expression. Thus, we can arrive at a "many datasets, one model" paradigm that as product requirements change, the data can quickly iterate and adapt on agile cycles.

In addition to affording more control over the product development process, synthetic data allows for 100% correct pixel by pixel tagging that opens the door for new modeling possibilities. Every image is tagged with the ground truth LaTeX for the expressions, bounding boxes per math character, and exact pixel masks for each character.

Our goal in releasing this dataset is to provide the data science and machine learning community with resources for undertaking the challenging computer vision task of extracting math expressions from images. The data offers something to all levels, from beginners building simple character recognition models to experts who wish to predict pixel-by-pixel masks and decode the complex structure of math expressions.

Content

The images contain math expressions of limits, a topic typically encountered by students learning Calculus I in the United States. Features of the writing such as font, writing utensils (type, color, pressure, consistency), angle and distance of photo, and size of writing are all simulated. Backgrounds features include shadows, various plain paper types, bleed throughs, other distortions, and noise typical of student taking photos of their math.

The strategy in defining the populations from which images are synthesized is to be a superset of what we expect students to submit. Therefore, the math expressions are not in themselves pedagogical, but aim to encompass the potential variety of student submissions, both mathematically correct and incorrect. The image features and augmentations are similarly designed to cover the range of possible student handwriting qualities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5602706%2F78c49b9673f8d07c91cd5c929e50ed13%2FPicture2.png?generation=1597361067979205&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5602706%2F38f70b6a773709eb02578f20634e8433%2FPicture1.png?generation=1597361068613807&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5602706%2F17a3a78ac635cd728f9d6ef32609aee8%2FPicture3.png?generation=1597361068784034&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5602706%2Fc052749a8085d66aa7bf97c78a4b6c6a%2FPicture4.png?generation=1597361068949074&alt=media%20=250x100" alt="">

Data consis...