OCR Datasets

This dataset contains 222,289 images of Chinese text in natural scenes. The collecting scenes of this dataset include indoor and outdoor scenes. The data diversity includes multiple scenes, and multiple shooting angles. For annotation, we have annotated in line-level, word-level, and character-level and well content matched text transcription included. The dataset can be used for OCR tasks in natural scenes.

This dataset contains 9,497 images of 10 types of forms. Rectangular bounding boxes were adopted to annotate forms. The dataset can be used for tasks such as forms detection.

This dataset contains 17,561 images of primary school mathematics papers. The data feature a pure color background. The data covers multiple question types, multiple types of test papers (math workbooks, test papers, competition test papers, etc.) and multiple grade levels. The dataset can be used for tasks such as intelligent scoring and homework guidance for primary school students.

This dataset contains 4,995 Vietnamese OCR images with annotations and text transcriptions. The data includes 258 natural scene images, 2,553 Internet images, and 2,184 document images. For line-level content annotation, quadrilateral bounding box annotations and text transcriptions are provided. For column-level content annotation, column-level quadrilateral bounding box annotation and text transcription are provided. The data can be used for tasks such as Vietnamese recognition in multiple scenes.

This dataset contains 104,320 images of Korean and Hindi text in natural scenes. The collecting scenes include packaging, posters, tickets, reminders, menus, building signs, and similar real-world environments. The dataset features diverse scenes, multiple shooting angles and multiple lighting conditions. For annotation, line-level polygon bounding box (or tetragon or rectangular bounding boxes) annotation, transcription and text attributes (language type) for the texts; vertical-level polygon bounding box (or tetragon or rectangular bounding boxes) annotation, transcription and text attributes (language type) for the text. The dataset can be used for Korean and Hindi OCR tasks in natural scenes.

This dataset contains 105,941 images of natural scene text collected across multiple real-world environments, covering 12 languages, including 6 Asian languages and 6 European languages. The data covers multiple natural scenes, multiple photographic angles. For annotation, each image is annotated with line-level quadrilateral bounding boxes and accurate text transcriptions. The data can be used for multilingual OCR, scene text recognition, and cross-language OCR model training.

This dataset contains handwritten text images collected from 100 individuals, including 50 Japanese, 49 Koreans and 1 Afghan. For different subjects, the corpus are different. The data diversity includes multiple cellphone models and different corpus. This dataset can be used for tasks such as handwriting OCR models, handwritten text recognition systems, and multilingual OCR pipelines

This dataset contains 4,538 Japanese handwritten text images collected from 101 individual writers, written on A4 paper. The dataset content including social livelihood, entertainment, tour, sport, movie, composition and other fields. For annotation, character-level rectangular bounding box annotation and text transcription and line-level rectangular bounding box annotation and text transcription were adopted. The dataset can be used for for training and evaluating Japanese handwriting OCR models, handwritten text recognition systems, and document understanding pipelines.

This dataset contains 1,000 Japanese invoice images, it includes 500 images with basic virtual editing and 500 images with professional editing. Data diversity includes different invoice contents, different editing types, and multiple invoice formats. The company name, address, name, fax number, phone number and other sensitive information on the invoice have been virtually edited and are not real information. The data can be used for tasks such as invoice detection, recognition, and end-to-end OCR tasks.