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Mobiusi/Scientific-Laboratory-Instrument-Readout-Recognition-Dataset

Scientific Laboratory Instrument Readout Recognition Dataset In modern scientific research, laboratory instruments are key sources of data acquisition, while manual recording of instrument readouts is inefficient and prone to errors. Traditional solutions such as manual transcription and basic OCR technology often prove inadequate when dealing with complex backgrounds, reflections, and multiple fonts of scientific instrument readings. This dataset aims to solve the problem of… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Scientific-Laboratory-Instrument-Readout-Recognition-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face
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Scientific Laboratory Instrument Readout Recognition Dataset

In modern scientific research, laboratory instruments are key sources of data acquisition, while manual recording of instrument readouts is inefficient and prone to errors. Traditional solutions such as manual transcription and basic OCR technology often prove inadequate when dealing with complex backgrounds, reflections, and multiple fonts of scientific instrument readings. This dataset aims to solve the problem of automatic recognition of diverse instrument readings in complex environments, improving the accuracy and efficiency of scientific work. The data is captured using high-resolution camera equipment under standard laboratory lighting conditions, and has undergone multiple rounds of quality inspection, including expert verification and consistency checks, ensuring the reliability of the data. The annotation team is composed of 20 professionals with backgrounds in physics, chemistry, and biology. The data undergoes pre-processing steps such as segmentation, normalization, and various noise treatments, stored in JPEG format, and is structured for quick access. The dataset achieves 99% accuracy in annotation and consistency better than 95%, ensuring data integrity. Advanced image pre-processing and improved OCR algorithms are used to enhance data parsing accuracy. It effectively solves the problem of manual data entry for scientific research, improving readout entry speed by over 50%. Compared with other datasets, it covers more instrument types and scenarios, with a rarity in optical character challenges, suitable for a wide range of scientific fields. It has good scalability and can be used in other fields such as industrial equipment readouts.

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
instrument_typestringIdentifies the type of instrument in the image, such as microscope or spectrometer.
reading_valuefloatThe instrument reading displayed in the image, such as temperature or concentration.
reading_unitstringThe unit of the instrument reading shown in the image, such as Celsius or Pascal.
display_textstringThe text or numerical content displayed on the instrument's screen.
reading_precisionfloatThe precision or error range of the instrument reading shown in the image.
brand_namestringIdentifies the brand name of the instrument in the image.
model_numberstringIdentifies the model number of the instrument in the image.
calibration_statusstringThe calibration status of the instrument in the image, such as calibrated or uncalibrated.
ambient_conditionsstringThe ambient conditions at the time the image was taken, like lighting and humidity.
instrument_conditionstringThe physical condition of the instrument in the image, such as intact or damaged.

Compliance Statement

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com