Optical Charater Recogntion

This dataset contains images of individual hand-written Bengali characters. Bengali characters (graphemes) are written by combining three components: a grapheme_root, vowel_diacritic, and consonant_diacritic. Your challenge is to classify the components of the grapheme in each image. There are roughly 10,000 possible graphemes, of which roughly 1,000 are represented in the training set. The test set includes some graphemes that do not exist in the train but has no new grapheme components. It takes a lot of volunteers filling out sheets like this to generate a useful amount of real data; focusing the problem on the grapheme components rather than on recognizing whole graphemes should make it possible to assemble a Bengali OCR system without handwriting samples for all 10,000 graphemes.

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.

This dataset consists of odometer or speedometer images of bike and car vehicles. ### Introduction This dataset can be used to detect or recognize odometer readings of the vehicles. Moreover, it can be used to classify the make of the car and bikes. The usecases can in the domain of insurance, repair and OCR. ### Dataset Features - Captured by 4000+ unique users - Rich in diversity - Mobile phone view point - Various lighting conditions - Digital and Analog Categories - Vehicle Model Types ### 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 on sales@datacluster.ai Visit www.datacluster.in to know more. Note: All the images are manually verified and are contributed by the large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 20,000+ original Number plate images captured and crowdsourced from over 700+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs Dataset Features - Dataset size : 20,000+ - Captured by : Over 4000+ crowdsource contributors - Resolution : 100% of images HD and above (1920x1080 and above) - Location : Captured with 700+ cities and villages across India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Number plate detection, ANPR, Number plate recognition, Self driving system, 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.

This dataset is an extremely challenging set of over 2000+ original Oximeter 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 300+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 50+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Oximeter Reading detection, Medical Device, Healthcare system detection, 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