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.