Content and Style Aware Generation of Text-Line Images for Handwriting Recognition.

Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art.

Electron doping in bottom-up engineered thermoelectric nanomaterials through HCl-mediated ligand displacement.

A simple and effective method to introduce precise amounts of doping in nanomaterials produced from the bottom-up assembly of colloidal nanoparticles (NPs) is described. The procedure takes advantage of a ligand displacement step to incorporate controlled concentrations of halide ions while removing carboxylic acids from the NP surface. Upon consolidation of the NPs into dense pellets, halide ions diffuse within the crystal structure, doping the anion sublattice and achieving n-type electrical doping. Through the characterization of the thermoelectric properties of nanocrystalline PbS, we demonstrate this strategy to be effective to control charge transport properties on thermoelectric nanomaterials assembled from NP building blocks. This approach is subsequently extended to PbTe(x)Se(1-x)@PbS core-shell NPs, where a significant enhancement of the thermoelectric figure of merit is achieved.