Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition

TL;DR

Hamming OCR introduces a lightweight scene text recognition model using locality sensitive hashing to encode characters, significantly reducing model size while maintaining competitive accuracy across multiple languages and datasets.

Contribution

The paper proposes a novel Hamming classifier with LSH encoding and a simplified transformer decoder, enabling vocabulary size-independent model parameters for scene text recognition.

Findings

Achieves competitive accuracy on multiple datasets.

Reduces model size independently of vocabulary size.

Effective for Chinese and multilingual text recognition.

Abstract

Recently, inspired by Transformer, self-attention-based scene text recognition approaches have achieved outstanding performance. However, we find that the size of model expands rapidly with the lexicon increasing. Specifically, the number of parameters for softmax classification layer and output embedding layer are proportional to the vocabulary size. It hinders the development of a lightweight text recognition model especially applied for Chinese and multiple languages. Thus, we propose a lightweight scene text recognition model named Hamming OCR. In this model, a novel Hamming classifier, which adopts locality sensitive hashing (LSH) algorithm to encode each character, is proposed to replace the softmax regression and the generated LSH code is directly employed to replace the output embedding. We also present a simplified transformer decoder to reduce the number of parameters by removing the feed-forward network and using cross-layer parameter sharing technique. Compared with traditional methods, the number of parameters in both classification and embedding layers is independent on the size of vocabulary, which significantly reduces the storage requirement without loss of accuracy. Experimental results on several datasets, including four public benchmaks and a Chinese text dataset synthesized by SynthText with more than 20,000 characters, shows that Hamming OCR achieves competitive results.