Video text recognition using sequential Monte Carlo and error voting methods
نویسندگان
چکیده
This paper addresses the issue of segmentation and recognition of text embedded in video sequences from their associated text image sequence extracted by a text detection module. To this end, we propose a probabilistic algorithm based on Bayesian adaptive thresholding and Monte-Carlo sampling. The algorithm approximates the posterior distribution of segmentation thresholds of text pixels in an image by a set of weighted samples. The set of samples is initialized by applying a classical segmentation algorithm on the first video frame and further refined by random sampling under a temporal Bayesian framework. One important contribution of the paper is to show that, thanks to the proposed methodology, the likelihood of a segmentation parameter sample can be estimated not using a classification criterion or a visual quality criterion based on the produced segmentation map, but directly from the induced text recognition result, which is directly relevant to our task. Furthermore, as a second contribution of the paper, we propose to align text recognition results from high confidence samples gathered over time, to composite a final result using error voting technique (ROVER) at the character level. Experiments are conducted on a two hour video database. Character recognition rates higher than 93%, and word error rates higher than 90% are achieved, which are 4 and 3% more than state-of-the-art methods applied to the same database. Index Terms Video text recognition, text segmentation, sequential monte-carlo filter, language model, recognition output voting error reduction.
منابع مشابه
Pruned Resampling: Probabilistic Model Selection Schemes for Sequential Face Recognition
This paper proposes probabilistic pruning techniques for a Bayesian video face recognition system. The system selects the most probable face model using model posterior distributions, which can be calculated using a Sequential Monte Carlo (SMC) method. A combination of two new pruning schemes at the resampling stage significantly boosts computational efficiency by comparison with the original o...
متن کاملText Detection and Recognition in Images and Video Sequences
Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values and complex backgrounds. This thesis investigates methods for building an efficient application system for detecting and recogn...
متن کاملEvaluating Quasi-Monte Carlo (QMC) algorithms in blocks decomposition of de-trended
The length of equal minimal and maximal blocks has eected on logarithm-scale logarithm against sequential function on variance and bias of de-trended uctuation analysis, by using Quasi Monte Carlo(QMC) simulation and Cholesky decompositions, minimal block couple and maximal are founded which are minimum the summation of mean error square in Horest power.
متن کاملSequential Monte Carlo video text segmentation
This paper presents a probabilistic algorithm for segmenting and recognizing text embedded in video sequences. The algorithm approximates the posterior distribution of segmentation thresholds of video text by a set of weighted samples. After initialization the set of samples is recursively refined by random sampling under a temporal Bayesian framework. The proposed methodology allows us to esti...
متن کاملTime-Varying Noise Estimation for Speech Enhancement and Recognition Using Sequential Monte Carlo Method
We present a method for sequentially estimating time-varying noise parameters. Noise parameters are sequences of time-varying mean vectors representing the noise power in the log-spectral domain. The proposed sequential Monte Carlo method generates a set of particles in compliance with the prior distribution given by clean speech models. The noise parameters in this model evolve according to ra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Pattern Recognition Letters
دوره 26 شماره
صفحات -
تاریخ انتشار 2005