U-DIADS-Bib is a proprietary dataset developed through the collaboration of computer scientists and humanities at the University of Udine. It is composed of 200 images, 50 for each of the 4 different manuscripts that characterize it. These handwritten books were selected in collaboration with humanist partners considering both the complexity of their layout and the presence of significant and semantically distinguishable elements. In particular, the images of the four manuscripts were collected from the digital library Gallica. All manuscripts are Latin and Syriac Bibles published between the 6th and 12th centuries A.D.

This dataset contains 2,000 images taken from inside a warehouse of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paraná. The images were acquired with three different cameras and are available in the JPG format with a resolution between 2,340 × 4,160 and 3,120 × 4,160 pixels. The dataset is split into three sets: training (800 images), validation (400 images) and testing (800 images). Every image has the following annotations available in a text file: the camera in which the image was taken, the counter’s position (x,y,w,h) and reading, as well as the position of each digit. All counters of the dataset (regardless of meter type) have 5 digits, and thus 10,000 digits were manually annotated. Source: Convolutional Neural Networks for Automatic Meter Reading

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Data collection: Finding a suitable source of data is considered a first step toward building a database. The first step in building a database is finding a suitable source. Here, the main goal is to collect images of Kurdish handwritten characters written by many writers. So, a form is designed to do so. The form is shown in Figure 1. It consists of 1 alphabet at a time letter that has been printed on the top right corner, and it has 125 empty blocks. The writers have been asked to write each letter three times in the three empty blocks. The total number of writers is 390. The forms have been distributed among two main categories: The academic staff of the Information Technology department at Tishk International University, the university students of the University of Kurdistan-Hawler, Salahaddin University, and Tishk International University As shown in Table 2. In total there were ten sets of forms, each set with 35 forms for 35 different letters, at first, we decided that nine sets, which will give us at least 1100 images for each letter were the best option for the time that we had. Then there were some problems with the collection process, in first prints of the forms there was confusion for instance in Set 2, there were 2 forms for the letter (چ) and none for (ج), and since we printed and distributed the form at the same time, we were not aware of this problem until the stage of pre-processing, This was creating an inconsistency in the number of samples that we had, for example by the 9th set we had 504 images of the letter (ڤ), which was much less than other letters that they had at least 1000 images. So we decided to add the 10th set as a complementary to other sets, it only contained those letter, which was missing in the first 9 forms, which was (ز،ژ،ش،غ،ڤ،ق،ک،ل،ن،ی), as explained in Table 3, the First column is the letter and columns 2-11 represent several images gathered in each set accordingly, while the first row the header row 2-36 are letters in each set, last row, and last columns are for the total of each letter and each set. Labeling and Organizing : Each image is labeled with three numbers and separated by an underscore, the first number is the id of the letter according to its positing in the alphabetical order which is shown in Table 4, the second number being the number of the set of form which there was 10 sets each giving to a specific group of writers, the third number is the order of that character in the form which was between 1 to 126, so each image had a label like following 02_01_94.jpg, 02 is the order of the letter which in this case is Alef (١), then 01 being in the set number 1 which was given to 4th-grade students of Information Technology department in Tishk International University, and 94 is the order of that image in the form. Each letter was stored in a folder with its ID as the name of that folder, with each folder containing approximately 1134 images of that letter.

Click to add a brief description of the dataset (Markdown and LaTe# Arabic Img2MD ## Dataset Summary The arabic-img2md dataset consists of 15,000 examples of PDF pages paired with their Markdown counterparts. The dataset is split into: - Train: 13,700 examples - Test: 1,520 examples This dataset was created as part of the open-source research project Arabic Nougat to enable OCR and Markdown extraction from Arabic documents. It contains mostly Arabic text but also includes examples with English text. X enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

The Belfort dataset This dataset includes minutes of Belfort municipal council drawn up between 1790 and 1946. Documents include deliberations, lists of councillors, convocations, and agendas. It includes 24,105 text-line images that were automatically detected from pages. Up to 4 transcriptions are available for each line image: two from humans, and two from automatic models. Files are organized in three folders: Images, Transcriptions, and Partitions. ## Images The dataset include 24,105 text-line images that were automatically detected using a generic Doc-UFCN model, and resized to a fixed height of 128 pixels. ## Transcriptions Up to 4 transcriptions are available for each image, as summarized in the following table: | Folder | N transcriptions | Description | Comments | |:----------: |-----------------: |-----------------------------|-----------------------------------------------------------------------------------| | callico_1/ | 24,105 | Human annotation n°1 | All lines have at least one human annotation | | callico_2/ | 8,878 | Human annotation n°2 | Only 33% of lines have two different human annotations | | dan/ | 24,102 | DAN automatic model | 3 images have empty transcriptions (no text was predicted by the model) | | pylaia/ | 23,536 | PyLaia automatic model | 569 images have empty transcriptions (no text was predicted by the model) | | rasa/ | 23,287 | RASA aggregation algorithm | 818 images have empty transcriptions | | rover/ | 24,104 | ROVER aggregation algorithm | 1 image has an empty transcription | ## Data partition We provide two distinct splits, both of them containing 19,013 training images, 2,262 validation images and 2,830 test images. * The Agreement-based split ensures the reliability of the test set: * The test set includes lines with perfect agreement between human annotators (Character Error Rate = 0%); * The validation set includes lines with good agreement between human annotators (0% < Character Error Rate < 5%); * The training set includes all the other lines. * The Random split is randomized. ## Evaluation Evaluation results in the paper are computed by comparing predictions to human annotations. Automatic and aggregated transcriptions are only used during model training.

A publicly available corpus of nineteenth-century newspaper text focused on crime in London, derived from the Gale British Library Newspapers corpus parts 1 and 2. The corpus comprises 600 newspaper excerpts and for each excerpt contains the original source image, the machine transcription of that image as found in the BLN and a gold standard manual transcription.

Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy.

This dataset contains 12,500 meter images acquired in the field by the employees of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units, across 395 cities and 1,113 locations (i.e., districts, villages and settlements), located in the Brazilian state of Paraná. Copel-AMR is composed of images captured in unconstrained scenarios, which typically include blur (due to camera motion), dirt, scale variations, in-plane and out-of-plane rotations, reflections, shadows, and occlusions. In 2,500 images (i.e., 20% of the dataset), it is not even possible to perform the meter reading due to occlusions or faulty meters. The images have a resolution of 480×640 or 640×480 pixels, depending on the orientation in which they were taken. Considering that the meter is operational and that there are no occlusions, these resolutions are enough for the meter reading to be legible. The dataset was randomly split as follows: 5,000 images for training, 5,000 images for testing and 2,500 images for validation, following the split protocol (i.e., 40%/40%/20%) used in the UFPR-AMR dataset. For reproducibility purposes, the subsets generated are explicitly available along with the Copel-AMR dataset. For each image in our dataset, we manually labeled the meter reading, the position (x, y) of each of the four corners of the counter, and a bounding box (x, y, w, h) for each digit. Corner annotations – which can be converted to a bounding box – enable the counter to be rectified, while bounding boxes enable the training of object detectors. Source: Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

Doc3DShade extends Doc3D with realistic lighting and shading. Follows a similar synthetic rendering procedure using captured document 3D shapes but final image generation step combines real shading of different types of paper materials under numerous illumination conditions. Source: Doc3DShade

Optical images of printed circuit boards as well as detailed annotations of any text, logos, and surface-mount devices (SMDs). There are several hundred samples spanning a wide variety of manufacturing locations, sizes, node technology, applications, and more. - pcb_image: Optical images of each PCB surface and rear, tagged with a unique identifier. - color_checker: Pallette to account for environmental illumination factors as well as a scale reference for the photo resolution. Each pcb image indicates which color checker it is associated with. - ocr_annotation: Optical Character Recognition annotations. This includes polygon boundaries around all relevant text on a PCB image. Whether the piece of text is on the board or a device, whether it is a logo or not, orientation, and more are noted within the columns of the csv. - smd_annotation: Surface-mount Device (SMD) annotations. This includes polygon boundaries around all relevant SMD devices such as resistors, capacitors, inductors, transistors, diodes, LEDs, and more. Along with each component, its associated silkscreen designator ('L', 'R', 'C', 'U', etc.) is recorded. - vtp_annotation: Vias, traces, and pins (VTP) annotations. These are regions of connectivity between SMDs on a PCB. Few annotations currently exist, this is considered in 'beta' mode currently. - metadata: Holds two files corresponding to information about image files. * pcb.csv holds information about the physical PCB samples such as their color, online item description, and any notes. * color_checker.csv indicates the pixels per millimeter (ppmm) of any image associated with that color checker, whether an X-Rite ColorChecker Passport or Nano was used, what camera performed the acquisition, and any relevant notes. Each annotation file is designed to be compatible with the S3A application (https://gitlab.com/ficsresearch/s3a or https://pypi.org/project/s3a/), a Python tool for visualizing polygon annotations on an image.

Introduced by Singh, Sumeet S.. “Teaching Machines to Code: Neural Markup Generation with Visual Attention.” ArXiv abs/1802.05415 (2018): n. pag. A prebuilt dataset for OpenAI's task for image-2-latex system. Includes total of ~140k formulas and images splitted into train, validation and test sets. Superset of im2latex-100K dataset.

IllusionChar_test ### Dataset Characteristics IllusionChar_test is a generated dataset containing 3,300 samples of images that feature sequences of 3 to 5 random characters. Unlike classification-focused datasets, this dataset is designed for tasks that require reasoning about patterns, sequences, or illusions within the character sequences. All images are synthetically generated, and no real-world data is included. ### Motivations and Content Summary The dataset was created using ControlNet for generating images and captions from four large language models (LLMs). It aims to incorporate the phenomenon of pareidolia, encouraging models to discern illusions or patterns within character sequences. By focusing on character-based sequences rather than classification, this dataset challenges multimodal models to analyze abstract combinations of symbols and interpret any illusory aspects. ### Potential Use Cases - Illusory VQA: Questioning models about potential illusions or patterns in the character sequences. - Sequence Reasoning Tasks: Evaluating a model’s ability to interpret and reason about ordered sequences. - Multimodal Model Evaluation: Benchmarking models on abstract and symbolic data with potential illusions. - Synthetic Data Research: Exploring synthetic datasets for challenging machine learning models with abstract reasoning tasks.

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It is composed of around 770k of color 256x256 RGB images extracted from the European Union Intellectual Property Office (EUIPO) open registry. Each of them is associated to multiple labels that classify the figurative and textual elements that appear in the images. These annotations have been classified by the EUIPO evaluators using the Vienna classification, a hierarchical classification of figurative marks. We suggest it to be used for: 1. Unconditional trademark generation 2. Conditional trademark generation. 3. Multi-label logo classification (Vienna classification). 4. Optical Character Recognition. 5. Conditional trade 6. Image segmentation. 7. Image retrieval

MatriVasha the largest dataset of handwritten Bangla compound characters for research on handwritten Bangla compound character recognition. The proposed dataset contains 120 different types of compound characters that consist of 306,464‬ images written where 152,950 male and 153,514 female handwritten Bangla compound characters. This dataset can be used for other issues such as gender, age, district base handwriting research because the sample was collected that included district authenticity, age group, and an equal number of men and women.

MNIST-MIX is a multi-language handwritten digit recognition dataset. It contains digits from 10 different languages. Source: https://github.com/jwwthu/MNIST-MIX

The NCSE v2.0 is a digitized collection of six 19th-century English periodicals The ground truth contains 358 cropped images of text blocks from 31 pages of 19th century newspaper data

PsOCR is a large-scale synthetic dataset for Optical Character Recognition in low-resource Pashto language.

Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning. Active Learning (AL) is an established method that minimizes human labeling effort by iteratively selecting the most informative unlabeled samples for expert annotation, thereby improving the overall classification performance. Even though AL has been known for decades [1], AL is still rarely used in real-world applications. As indicated in the two community web surveys among the NLP community about AL [2], [3], two main reasons continue to hold practitioners back from using AL: first, the complexity of setting AL up, and second, a lack of trust in its effectiveness. We hypothesize that both reasons share the same culprit: the large hyperparameter space of AL. This mostly unexplored hyperparameter space often leads to misleading and irreproducible AL experiment results. In this study, we first compiled a large hyperparameter grid of over 4.6 million hyperparameter combinations, second, recorded the performance of all combinations in the so-far biggest conducted AL study, and third, analyzed the impact of each hyperparameter in the experiment results. Besides our publication we are making our raw experiment results publically available for other researchers to built upon. [1] B. Settles, “Active Learning Literature Survey,” University of Wisconsin-Madison Department of Computer Sciences, Technical Report, 2009. [2] K. Tomanek and F. Olsson, “A web survey on the use of active learning to support annotation of text data,” in Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, E. Ringger, R. Haertel, and K. Tomanek, Eds. Boulder, Colorado: Association for Computational Linguistics, 2009, pp. 45–48. [3] J. Romberg, C. Schröder, J. Gonsior, K. Tomanek, and F. Olsson, “Have LLMs Made Active Learning Obsolete? Surveying the NLP Community,” 2025.

This paper introduces a new large-scale dataset for Farsi document images, named SUT, which aims to tackle the challenges associated with obtaining diverse and substantial ground-truth data for supervised models in document image analysis (DIA) tasks, like document image classification, text detection and recognition, and information retrieval. The dataset comprises 62,453 images that have been categorized into 21 distinct classes, including identity documents featuring synthetically generated personal information superimposed on various backgrounds. The dataset also includes corresponding files with labeling information for the images. The ground-truth data is organized in CSV files containing image file paths and associated information about the embedded data.

The UTRSet-Real dataset is a comprehensive, manually annotated dataset specifically curated for Printed Urdu OCR research. It contains over 11,000 printed text line images, each of which has been meticulously annotated. One of the standout features of this dataset is its remarkable diversity, which includes variations in fonts, text sizes, colours, orientations, lighting conditions, noises, styles, and backgrounds. This diversity closely mirrors real-world scenarios, making the dataset highly suitable for training and evaluating models that aim to excel in real-world Urdu text recognition tasks. The availability of the UTRSet-Real dataset addresses the scarcity of comprehensive real-world printed Urdu OCR datasets. By providing researchers with a valuable resource for developing and benchmarking Urdu OCR models, this dataset promotes standardized evaluation and reproducibility and fosters advancements in the field of Urdu OCR. Further, to complement the UTRSet-Real for training purposes, we also present UTRSet-Synth, a high-quality synthetic dataset closely resembling real-world representations of Urdu text. For more information and details about the UTRSet-Real & UTRSet-Synth datasets, please refer to the paper "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents"

The UTRSet-Synth dataset is introduced as a complementary training resource to the UTRSet-Real Dataset, specifically designed to enhance the effectiveness of Urdu OCR models. It is a high-quality synthetic dataset comprising 20,000 lines that closely resemble real-world representations of Urdu text. To generate the dataset, a custom-designed synthetic data generation module which offers precise control over variations in crucial factors such as font, text size, colour, resolution, orientation, noise, style, and background, was employed. Moreover, the UTRSet-Synth dataset tackles the limitations observed in existing datasets. It addresses the challenge of standardizing fonts by incorporating over 130 diverse Urdu fonts, which were thoroughly refined to ensure consistent rendering schemes. It overcomes the scarcity of Arabic words, numerals, and Urdu digits by incorporating a significant number of samples representing these elements. Additionally, the dataset is enriched by randomly selecting words from a vocabulary of 100,000 words during the text generation process. As a result, UTRSet-Synth contains a total of 28,187 unique words, with an average word length of 7 characters. The availability of the UTRSet-Synth dataset, a synthetic dataset that closely emulates real-world variations, addresses the scarcity of comprehensive real-world printed Urdu OCR datasets. By providing researchers with a valuable resource for developing and benchmarking Urdu OCR models, this dataset promotes standardized evaluation, and reproducibility, and fosters advancements in the field of Urdu OCR. For more information and details about the UTRSet-Real & UTRSet-Synth datasets, please refer to the paper "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents"

Dataset Introduction This dataset leverages VideoDB's Public Collection to offer a diverse range of videos featuring text-containing scenes. It spans multiple categories—ranging from finance and legal documents to software UI elements and handwritten notes—ensuring a broad representation of real-world text appearances. Each video is annotated with frame indexes to facilitate consistent and reproducible OCR benchmarks. Currently, the dataset includes over 25 curated videos, yielding thousands of extracted frames that present a variety of text-related challenges. ### Key Features 1. Diverse Text Genres - Finance/Business: Includes news tickers and stock market visuals where text scrolls rapidly. - Legal/Educational: Features documents with formal language, diagrams, and formatted text. - Software/Web Development/UI: Shows on-screen code editors, browser windows, and other UI elements that test OCR's ability to handle varying font sizes and code snippets. - Handwriting: Encompasses both cursive and print handwriting on whiteboards or paper, capturing the challenges of style variability and penmanship. - Miscellaneous/Other: Covers signage, billboards, and everyday text in the wild. 2. Rich Annotation - Each video includes frame indexes or scene timestamps to ensure consistent, reproducible extraction of text segments. - Ground truth (OCR text) is provided for thousands of extracted frames, facilitating quantitative performance evaluations (e.g., CER, WER). 3. Benchmark-Ready - The dataset seamlessly integrates with the ocr-benchmark repository to streamline model evaluation. - Scripts are included for frame extraction, automatic OCR comparison, and metric calculation (CER, WER, accuracy). ### How to Access - VideoDB Public Collection ID: c-c0a2c223-e377-4625-94bf-910501c2a31c Simply reference this ID within VideoDB to retrieve and review the videos. - Ground Truth Files: Located in the ocr_ground_truths directory. The JSON files map each frame to its corresponding textual annotations. For detailed instructions on working with VideoDB Public Collections, please refer to the official documentation. ### Licensing and Usage - Usage Restrictions: The videos are publicly accessible for research and educational use. - Attribution: Please cite the Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments paper or repository if you use this dataset in your work.

WebLI (Web Language Image) is a web-scale multilingual image-text dataset, designed to support Google’s vision-language research, such as the large-scale pre-training for image understanding, image captioning, visual question answering, object detection etc. The dataset is built from the public web, including image bytes, image-associated texts (alt-text, OCR, page title), 109 languages and many other features. The dataset is deduplicated on 68 common vision/vision-language tasks, and has no user or personally identifiable data with careful RAI considerations. source: PaLI: A Jointly-Scaled Multilingual Language-Image Model

The Burmese Handwritten Digit Dataset (BHDD) is a dataset project specifically created for recognizing handwritten Burmese digits. It is a Burmese version of MNIST dataset with a training set of 60,000 examples, and a test set of 27,561 examples. ## Overview Dataset Statistics: - Training Set: 60,000 samples - Testing Set: 27,561 samples - Number of Classes: 10 (Burmese digits 0–9) Data Format: - Train Image Shape: (60000, 784) - Train Label Shape: (60000, 10) - Test Image Shape: (27561, 784) - Test Label Shape: (27561, 10) The dataset was collected from over 150 individuals of different ages (ranging from high school students to professionals in their 50s) and diverse occupations (including clerks, programmers, and others) to achieve a wide variety of handwriting styles. We then preprocessed to mirror the structure and functionality of MNIST. ## Dataset Content The dataset consists of: 1. Train Images: 60,000 grayscale images of handwritten Burmese digits, flattened into a 1D array of size 784 (28x28 pixels). 2. Train Labels: One-hot encoded labels corresponding to the digit class. 3. Test Images: 27,561 grayscale images for testing purposes. 4. Test Labels: One-hot encoded labels for testing data. ## Contribution We encourage the ML/DL community to contribute by: - Creating digit recognizers. - Benchmarking with different models and algorithms. - Writing tutorials and sharing findings. ## Citation If you use the BHDD dataset in your work, please cite this repository: @dataset{bhdd, author = {Expa.AI Research Team}, title = {Burmese Handwritten Digit Dataset (BHDD)}, year = {2019}, url = {https://github.com/baseresearch/BHDD} } ## Acknowledgments This dataset would not have been possible without: - The efforts of the Expa.AI Research Team. - Volunteers and interns from Taungoo Computer University who contributed handwriting samples. - Highschool students from St.Augustine / B.E.H.S (2) Kamayut - Friends and family members of Expa.AI Research Team. - The community’s ongoing support and interest in ML/DL for the Burmese language. ## License This dataset is released under the LGPL-3.0 license. Please see the LICENSE file for more details.

CodeSCAN is the first large-scale and diverse dataset of coding screenshots with pixel-perfect annotations. It features: - 24 popular programming languages (according to Github) - 100 random repositories per language (with MIT, BSD-3 or WTFPL License), i.e. 2.400 repositories in total - Per repository we use 5 files, i.e. 12.000 files in total - ~100 different themes and 25 different fonts - Diverse layouts changes, such as menu bar visibility, sidebar position, output window content, etc. - Numerous realistic interactions such as searching, typing and selecting within a file, etc. Check our project page (https://a-nau.github.io/codescan/) for details.

A dataset with $23\,870$ digital trajectories (i.e. time series) of handwritten lower- and uppercase Latin letters and Arabic numbers ($a$-$z$, $A$-$Z$, $0$-$9$), generated by $77$ experts using a Wacom Pen Tablet. An expert is considered a proficient user of the recorded symbols, in this case adult native German speakers. DigiLetTs was created to extend the Omniglot dataset and contains five variants per character per subject to allow the quantification of intra-subject variability and to assess and account for individual writing styles. The determination and imitation of subject-dependent writing styles is introduced as a new task in this paper. For more information about the dataset, please refer to the repository (Homepage button below).

This dataset is an extremely challenging set of over 5000+ original Hindi text images captured and crowdsourced from over 700+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at DataclusterLabs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 700+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 400+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Hindi text detection, Hindi NLP, Text recognition, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

Introduction The dataset consists of Indian traffic signs images for classification and detection. The images have been taken in varied weather conditions in daylight, evening and nights. The dataset has a wide variety of variations of illumination, distances, view points etc. This dataset represents a very challenging set of unstructured images of Indian traffic signboards. ### Dataset Features - Captured by 2000+ unique users - Covers wide variety of Indian traffic signs - Captured with 20+ cities accross India - Captured using mobile phones - Highly diverse - Various lighting conditions like day, night, - Outdoor scene with variety of view points ### Dataset Features - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

The Turkish Scene Text Recognition (TS-TR) dataset was primarily developed to fill the gap in non-English text recognition resources, specifically addressing the unique challenges presented by the Turkish language, such as special characters and diacritics. This dataset mirrors real-world conditions with texts displayed in various fonts, sizes, orientations, and complex backgrounds from multiple urban and rural environments. Such diversity ensures the training of models that can generalize across different scenarios, including varying lighting conditions and complex visual layouts.

This dataset is an extremely challenging set of over 2000+ original Visiting card/ID card images captured and crowdsourced from over 300+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 2000+ - Captured by : Over 150+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 300+ cities accross India - Diversity : Covers a wide variety of things such as reflective paper, different fonts, different color and type of cards, etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Visiting card detection, card edge detection, paper edge detection, ID card OCR, Hindi OCR, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform