GLM-OCR Technical Report

• URL: http://arxiv.org/abs/2603.10910v2
• Date: Mon, 16 Mar 2026 13:48:45 GMT
• Title: GLM-OCR Technical Report
• Abstract summary: GLM-OCR is an efficient compact model designed for real-world document understanding.<n>It combines a CogViT visual encoder with a GLM language decoder, achieving a strong balance between computational efficiency and recognition performance.<n>Extensive evaluations on public benchmarks and industrial scenarios show that GLM-OCR achieves competitive or state-of-the-art performance.
• Score: 65.42028025507491
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding. It combines a 0.4B-parameter CogViT visual encoder with a 0.5B-parameter GLM language decoder, achieving a strong balance between computational efficiency and recognition performance. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. At the system level, a two-stage pipeline is adopted: PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. Extensive evaluations on public benchmarks and industrial scenarios show that GLM-OCR achieves competitive or state-of-the-art performance in document parsing, text and formula transcription, table structure recovery, and key information extraction. Its compact architecture and structured generation make it suitable for both resource-constrained edge deployment and large-scale production systems.

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