Hinting Pipeline and Multivariate Regression CNN for Maize Kernel Counting on the Ear

Maize is a highly nutritional cereal widely used for human and animal consumption and also as raw material by the biofuels industries. This highlights the importance of precisely quantifying the corn grain productivity in season, helping the commercialization process, operationalization, and critical decision-making. Considering the manual labor cost of counting maize kernels, we propose in this work a novel preprocessing pipeline named hinting that guides the attention of the model to the center of the corn kernels and enables a deep learning model to deliver better performance, given a picture of one side of the corn ear. Also, we propose a multivariate CNN regressor that outperforms single regression results. Experiments indicated that the proposed approach excels the current manual estimates, obtaining MAE of 34.4 and R2 of 0.74 against 35.38 and 0.72 for the manual estimate, respectively.

Pay Voice: Point of Sale Recognition for Visually Impaired People

Millions of visually impaired people depend on relatives and friends to perform their everyday tasks. One relevant step towards self-sufficiency is to provide them with means to verify the value and operation presented in payment machines. In this work, we developed and released a smartphone application, named Pay Voice, that uses image processing, optical character recognition (OCR) and voice synthesis to recognize the value and operation presented in POS and PIN pad machines, and thus informing the user with auditive and visual feedback. The proposed approach presented significant results for value and operation recognition, especially for POS, due to the higher display quality. Importantly, we achieved the key performance indicators, namely, more than 80% of accuracy in a real-world scenario, and less than $5$ seconds of processing time for recognition. Pay Voice is publicly available on Google Play and App Store for free.