The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels. Source: Measuring Human Perception to Improve Handwritten Document Transcription Image Source: https://fki.tic.heia-fr.ch/databases/iam-handwriting-database

This dataset arises from the READ project (Horizon 2020). The dataset consists of a subset of documents from the Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805 (about 30.000 pages), which will be used in the READ project. This dataset is written in Early Modern German. The number of writers is unknown. Handwriting in this collection is complex enough to challenge the HTR software. The training dataset is composed of 400 pages; most of the pages consist of a single block with many difficulties for line detection and extraction. The ground-truth in this set is in PAGE format and it is provided annotated at line level in the PAGE files. The previous dataset is the same that is located at https://zenodo.org/record/218236#.WnLhaCHhBGF The new file includes the test set corresponding to the HTR competition held at ICFHR 2016 Toselli, A.H., Romero, V., Villegas, M., Vidal, E., & Sánchez, J.A. (2018). HTR Dataset ICFHR 2016 (Version 1.2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1297399

The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels. Source: Measuring Human Perception to Improve Handwritten Document Transcription Image Source: https://fki.tic.heia-fr.ch/databases/iam-handwriting-database

This dataset arises from the READ project (Horizon 2020). The dataset consists of a subset of documents from the Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805 (about 30.000 pages), which will be used in the READ project. This dataset is written in Early Modern German. The number of writers is unknown. Handwriting in this collection is complex enough to challenge the HTR software. The training dataset is composed of 400 pages; most of the pages consist of a single block with many difficulties for line detection and extraction. The ground-truth in this set is in PAGE format and it is provided annotated at line level in the PAGE files. The previous dataset is the same that is located at https://zenodo.org/record/218236#.WnLhaCHhBGF The new file includes the test set corresponding to the HTR competition held at ICFHR 2016 Toselli, A.H., Romero, V., Villegas, M., Vidal, E., & Sánchez, J.A. (2018). HTR Dataset ICFHR 2016 (Version 1.2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1297399

Bentham manuscripts refers to a large set of documents that were written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). Volunteers of the Transcribe Bentham initiative transcribed this collection. Currently, >6 000 documents or > 25 000 pages have been transcribed using this public web platform. For our experiments, we used the BenthamR0 dataset a part of the Bentham manuscripts.

The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by LATEX which subsequently was filled out by persons with their handwriting. The database consists of more than 1400 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 diferent writers. We utilized three different datasets described as following: Handwritten samples (Forms) of keywords in Kazakh and Russian (Areas, Cities , Village , etc.) Handwritten Kazakh and Russian alphabet in cyrillic Handwritten samples (Forms) of poems in Russian Image source: https://github.com/abdoelsayed2016/HKR_Dataset

Handwritten Text Recognition (HTR) is an open problem at the intersection of Computer Vision and Natural Language Processing. The main challenges, when dealing with historical manuscripts, are due to the preservation of the paper support, the variability of the handwriting – even of the same author over a wide time-span – and the scarcity of data from ancient, poorly represented languages. With the aim of fostering the research on this topic, in this paper we present the Ludovico Antonio Muratori (LAM) dataset, a large line-level HTR dataset of Italian ancient manuscripts edited by a single author over 60 years. The dataset comes in two configurations: a basic splitting and a date-based splitting which takes into account the age of the author. The first setting is intended to study HTR on ancient documents in Italian, while the second focuses on the ability of HTR systems to recognize text written by the same writer in time periods for which training data are not available. For both configurations, we analyze quantitative and qualitative characteristics, also with respect to other line-level HTR benchmarks, and present the recognition performance of state-of-the-art HTR architectures. The dataset is available for download at https://aimagelab.ing.unimore.it/go/lam.

Digital Peter is a dataset of Peter the Great's manuscripts annotated for segmentation and text recognition. The dataset may be useful for researchers to train handwriting text recognition models as a benchmark for comparing different models. It consists of 9,694 images and text files corresponding to lines in historical documents. The dataset includes Peter’s handwritten materials covering the period from 1709 to 1713. The open machine learning competition Digital Peter was held based on the considered dataset.

We introduce a new Dataset (BN-HTRd) for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus - which acted as ground truth texts for the handwritings. Our dataset contains a total of 786 full-page images collected from 150 different writers. With a staggering 1,08,181 instances of handwritten words, distributed over 14,383 lines and 23,115 unique words, this is currently the 'largest and most comprehensive dataset' in this field. We also provided the bounding box annotations (YOLO format) for the segmentation of words/lines and the ground truth annotations for full-text, along with the segmented images and their positions. The contents of our dataset came from a diverse news category, and annotators of different ages, genders, and backgrounds, having variability in writing styles. The BN-HTRd dataset can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spotting, word/line segmentation, and so on. The statistics of the original dataset are given below: - Number of writers = 150 - Total number of images = 786 - Total number of lines = 14,383 - Total number of words = 1,08,181 - Total number of unique words = 23,115 - Total number of punctuation = 7,446 - Total number of characters = 5,74,203 From v3.0 onwards, we are also providing automatic bounding box annotations (YOLO format) of 805 document images containing words/lines. The statistics of the automatic annotations are given below: - Number of writers = 87 - Total number of images = 805 - Total number of lines = 14,836 - Total number of words = 1,06,135

Saint Gall dataset contains handwritten historical manuscripts written in Latin that date back to the 9th century. It consists of 60 pages, 1 410 text lines and 11 597 words.

Description We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents. Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction. The dataset is available at https://zenodo.org/record/7868059 ## Details for each series and entity type | Series | Train | Validation | Test | Total (%) | | ------------------- | ----- | ---------- | ---- | --------: | | E series | 322 | 64 | 79 | 8.6 | | L series | 38 | 8 | 4 | 0.9 | | M series | 128 | 21 | 27 | 3.3 | | X1a series | 2209 | 491 | 469 | 58.8 | | Y series | 940 | 205 | 196 | 24.9 | | Douët s'Arcq series | 141 | 22 | 29 | 3.5 | | Total | 3778 | 811 | 804 | 100 | | Entities | Train | Validation | Test | Total (%) | | -------------- | ----- | ---------- | ----- | --------: | | date | 8406 | 1814 | 1799 | 10.4 | | title | 35531 | 7495 | 8173 | 44.5 | | serie | 3168 | 664 | 676 | 3.9 | | analysis | 25988 | 5130 | 5602 | 31.9 | | volume_number | 3913 | 808 | 813 | 4.8 | | article_number | 3181 | 665 | 678 | 3.9 | | arrangement | 644 | 122 | 153 | 0.8 | | Total | 80831 | 16698 | 17894 | 100 | ## Data encoding Transcriptions with entities are encoded in the labels.json JSON file. Special tokens are used to represent named entities. Please not that there are only opening NER tokens: each entity spans all words until the next entity starts. | Entities | Special token | Symbol unicode | | -------------- | ------------- | -------- | | date | ⓓ | \u24d3 | | title | ⓘ | \u24d8 | | serie | ⓢ | \u24e2 | | analysis | ⓒ | \u24d2 | | volume_number | ⓟ | \u24df | | article_number | ⓐ | \u24d0 | | arrangement | ⓥ | \u24e5 | # Cite us! The dataset is presented in details in the following article: bib @article{simara2023, author = {Solène Tarride and Mélodie Boillet and Jean-François Moufflet and Christopher Kermorvant}, title = {SIMARA: a database for key-value information extraction from full-page handwritten documents}, year = {2023}, journal={Proceedings of the 17th International Conference on Document Analysis and Recognition}, }

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.

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.

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.

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.