Historically, sign languages have been used as a way of communication by people with hearing loss. However, it is not trivial for other people to adopt sign language, due to its particular grammatical structure, rules, vocabulary, and so on. Recently, commercial wearable devices based on the electromyography technology became available to the wide audience at accessible cost. This work shows the process of creating a data set of electromyographic sign records for the Brazilian Sign Language (Língua Brasileira de Sinais - LIBRAS). The authors believe that, using such data set, it is possible to apply machine learning techniques to automatically recognize and translate Brazilian Sign Language to other languages, and thus improve the communication. To demonstrate the dataset potential, some classification methods were applied, achieving up to 89% of correctness.

Multi-script writer identification consists in identifying a person of a given text written in one script from the samples of the same person written in another script. The rationale behind this is that the writing style of an individual remains constant across different scripts. While this hypothesis may hold, recent results on a multi-script writer identification competition show that classical writer-dependent classifiers fail in this task. In this work we investigate the efficacy of a writer-independent classifier based on dissimilarity for multi-script writer identification. The classifiers were trained using two different texture descriptors (LBP and LPQ). Our experiments on 475 writers of the QUWI dataset, which is composed of Arabic and English samples, show that the proposed strategy surpasses the results published in the literature by a large margin, achieving error rates similar to single-script writer identification systems.

In this study we assess the performance of textural descriptors for writer identification on different writing styles and also on forgeries. To do that, we have performed a series of experiments using the Fire maker database, which provides for the same writer texts written on three different writing styles and also copied forged text. Our experimental protocol is based on the dissimilarity framework and SVM classifiers, which were trained with LBP (Local Binary Pattern) and LPQ (Local Phase Quantization). The 250 writers of the database were divided into different configurations to observe the impacts of different sizes of the training set on the performance of the system. Our experimental results corroborates the fact that the texture is an interesting alternative for writer identification. The classifier trained with LPQ was able to produce error rates 23 percentage points smaller than those reported in the literature for upper-case and free writing styles. Regarding the forgeries, the LPQ-based classifier goes further reducing the error rate up to 44 percentage points depending on the writing style used for training.

In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the main strokes of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably.