TY - CONF AU - Anjan Dutta AU - Pau Riba AU - Josep Llados AU - Alicia Fornes A2 - ICDAR PY - 2017// TI - Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification BT - 14th International Conference on Document Analysis and Recognition SP - 33 EP - 38 KW - graph embedding KW - hierarchical graph representation KW - graph clustering KW - stochastic graphlet embedding KW - graph classification N2 - Document pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE).Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with supportvector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical symbols L1 - http://refbase.cvc.uab.es/files/DRL2017.pdf UR - http://dx.doi.org/10.1109/ICDAR.2017.15 N1 - DAG; 600.097; 601.302; 600.121 ID - Anjan Dutta2017 ER -