Word Recognition with Deep Conditional Random Fields

TL;DR

This paper introduces a novel deep CRF model for handwritten word recognition, combining deep feature learning with sequence modeling to improve accuracy over traditional methods.

Contribution

It presents a unified deep learning framework that integrates feature extraction and sequence labeling for improved handwritten word recognition.

Findings

Deep CRFs outperform baseline models on two datasets.

Pre-training with RBMs enhances feature quality.

Unified training improves recognition accuracy.

Abstract

Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--which can perform poorly with new data sets. Recently, deep learning has attracted great attention because of the ability to learn features from raw data. Moreover they have yielded state-of-the-art results in classification tasks including character recognition and scene recognition. On the other hand, word recognition is a sequential problem where we need to model the correlation between characters. In this paper, we propose using deep Conditional Random Fields (deep CRFs) for word recognition. Basically, we combine CRFs with deep learning, in which deep features are learned and sequences are labeled in a unified framework. We pre-train the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize the entire network with an online learning algorithm. The proposed model was evaluated on two datasets, and seen to perform significantly better than competitive baseline models. The source code is available at https://github.com/ganggit/deepCRFs.