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Measuring the width of sliding drops, a key parameter linked to frictional forces, requires additional equipment like cameras or mirrors, complicating experimental setups and limiting observable areas. This study introduces a novel method that simplifies the measurement process by employing artificial neural networks to estimate millimeter-scale drop width directly from side-view video data. Our approach processes raw video footage to dynamically identify features most indicative of drop width. By treating drop behavior as an extrinsic time-series problem, our model effectively captures temporal dependencies in video sequences. We propose a VGG8-inspired architecture optimized for small and low information density video datasets. This architecture is combined with our novel position invariant video processing methodology that efficiently removes non-essential regions, reducing computation time by 84%. We further integrate ConvTran, a state-of-the-art time-series classification model, with an enhanced Absolute Position Encoding, improving the encoding\u2019s dot-product and lowering drop width estimation errors. Our novel neural network architecture achieved a root mean square error of 48 <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\upmu $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u03bc<\/mml:mi>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>m (1.7\u00a0% relative error), where each pixel corresponds to approximately 44 <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\upmu $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u03bc<\/mml:mi>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>m. 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