43 research outputs found
Online handwritten mathematical expression recognition
This thesis presents a system for online handwritten mathematical expression recognition that involves integrals, summation notation, superscripts and subscripts, square-roots, fractions, trigonometric and logarithmic functions; together with a user-interface for writing scientific article. The aim of this study is to utilize the most convenient man-machine-interface, a pen, for input of mathematical expressions. In pen-enabled devices, handwriting sequences are collected by the digitization of pen movements which outputs an array of coordinates called strokes. A neural network is trained for recognizing each stroke and a recursive algorithm parses the expression by combining neural network output and structure of the expression. The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC-API for recognizing textual input and also supports conversion of hand-drawn figures into PNG format, which enable the user to enter text, mathematics and draw figures in a single interface. After the recognition, all output is combined into one LATEX code and compiled into a PDF file.s The system presented in this thesis provides a natural interface, hence enables easyinput of mathematical expressions in all pen-enabled devices such as tablet PCs, PDAs, external tablet pads, electronic pen-boards etc
Symbol detection in online handwritten graphics using Faster R-CNN
Symbol detection techniques in online handwritten graphics (e.g. diagrams and
mathematical expressions) consist of methods specifically designed for a single
graphic type. In this work, we evaluate the Faster R-CNN object detection
algorithm as a general method for detection of symbols in handwritten graphics.
We evaluate different configurations of the Faster R-CNN method, and point out
issues relative to the handwritten nature of the data. Considering the online
recognition context, we evaluate efficiency and accuracy trade-offs of using
Deep Neural Networks of different complexities as feature extractors. We
evaluate the method on publicly available flowchart and mathematical expression
(CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used
on both datasets, enabling the possibility of developing general methods for
symbol detection, and furthermore, general graphic understanding methods that
could be built on top of the algorithm.Comment: Submitted to DAS-201
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition
Handwritten mathematical expression recognition (HMER) has attracted
extensive attention recently. However, current methods cannot explicitly study
the interactions between different symbols, which may fail when faced similar
symbols. To alleviate this issue, we propose a simple but efficient method to
enhance semantic interaction learning (SIL). Specifically, we firstly construct
a semantic graph based on the statistical symbol co-occurrence probabilities.
Then we design a semantic aware module (SAM), which projects the visual and
classification feature into semantic space. The cosine distance between
different projected vectors indicates the correlation between symbols. And
jointly optimizing HMER and SIL can explicitly enhances the model's
understanding of symbol relationships. In addition, SAM can be easily plugged
into existing attention-based models for HMER and consistently bring
improvement. Extensive experiments on public benchmark datasets demonstrate
that our proposed module can effectively enhance the recognition performance.
Our method achieves better recognition performance than prior arts on both
CROHME and HME100K datasets.Comment: 12 Page
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Handwritten mathematical expression recognition is a challenging problem due
to the complicated two-dimensional structures, ambiguous handwriting input and
variant scales of handwritten math symbols. To settle this problem, we utilize
the attention based encoder-decoder model that recognizes mathematical
expression images from two-dimensional layouts to one-dimensional LaTeX
strings. We improve the encoder by employing densely connected convolutional
networks as they can strengthen feature extraction and facilitate gradient
propagation especially on a small training set. We also present a novel
multi-scale attention model which is employed to deal with the recognition of
math symbols in different scales and save the fine-grained details that will be
dropped by pooling operations. Validated on the CROHME competition task, the
proposed method significantly outperforms the state-of-the-art methods with an
expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME
2016, by only using the official training dataset