Web Framework for Evaluating Handwritten Math Expression Recognition
نویسندگان
چکیده
In this paper we present a new web framework for compiling and publishing evaluation results for CROHME 2016. The framework makes user easier to view and organize handwritten math expression recognition evaluation results produced by the LgEval library. LgEval is used for evaluating structural pattern recognition systems, which was originally developed as the standard evaluation tool for the CROHME competition. It consist of a set of command line tools running with bash for evaluating, visualizing and translating label graphs. The library could also be used to represent and evaluate structure for other problems which could be reduced to label graph format. The web framework integrates LgEval as a core module, and executes corresponding functions when user submit the math expression pattern recognition result to the server. Our system reaches the goal of transparency and accuracy. Keywords-handwritten math recognition; evaluation system; label graph; web framework
منابع مشابه
Evaluating structural pattern recognition for handwritten math via primitive label graphs
Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online ha...
متن کاملPersian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...
متن کاملFeature Evaluation for Handwritten Character Recognition with Regressive and Generative Hidden Markov Models
Hidden Markov Models constitute an established approach often employed for offline handwritten character recognition in digitized documents. The current work aims at evaluating a number of procedures frequently used to define features in the character recognition literature, within a common Hidden Markov Model framework. By separating model and feature structure, this should give a more clear i...
متن کاملMulti-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, recently we propose the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. I...
متن کاملNeural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten
Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016