arXiv subjects

Michael Blumenstein

Publications and source records attributed to Michael Blumenstein.

21 records · Page 2Linked to original sources

arXiv2019

An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting

The handwriting of an individual may vary substantially with factors such as mood, time, space, writing speed, writing medium and tool, writing topic, etc. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of a person, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from offline Bengali handwriting of high intra-variability. To this end, we use various models mainly based on handcrafted features with SVM (Support Vector Machine) and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results.

cs.CV↗

arXiv2018

Bag-of-Visual-Words for Signature-Based Multi-Script Document Retrieval

An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a Support Vector Machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e. signature strokes) and the background spatial information (i.e. background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The `Tobacco' document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents and promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.

cs.CV↗

arXiv2017

Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models

As the population of world is increasing, and even more concentrated in urban areas, ensuring public safety is becoming a taunting job for security personnel and crowd managers. Mass events like sports, festivals, concerts, political gatherings attract thousand of people in a constraint environment,therefore adequate safety measures should be adopted. Despite safety measures, crowd disasters still occur frequently. Understanding underlying dynamics and behavior of crowd is becoming areas of interest for most of computer scientists. In recent years, researchers developed several models for understanding crowd dynamics. These models should be properly calibrated and validated by means of data acquired in the field. In this paper, we developed a computer vision tool set that can be helpful not only in initializing the crowd simulation models but can also validate the simulation results. The main features of proposed tool set are: (1) Crowd flow segmentation and crowd counting, (2) Identifying source/sink location for understanding crowd behavior, (3) Group detection and tracking in crowds.

cs.CV↗