TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters

Honglie Wang; Jia Sun; Zijun Li; Junlong Wu; Pengcheng Wei; Jiyuan Wang; Yongrui Heng; Boheng Zhang

cs.CVAug 20, 2026

TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters

Authors: Honglie Wang, Jia Sun, Zijun Li, Junlong Wu, Pengcheng Wei, Jiyuan Wang, Yongrui Heng, Boheng Zhang,

Organizations: Kuaishou Technology · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Abstract

Text editing in product posters entails inserting new text or replacing existing text while preserving product appearance, background content, and global composition. Despite recent progress in instruction-based image editing, general-purpose models remain unreliable in this setting: they often omit or incorrectly render the target text, place it over salient products or pre-existing content, and produce structurally distorted or visually inconsistent glyphs. We introduce TextRefine, a task-aligned post-training framework that combines supervised fine-tuning with operation-specific reward optimization to address these complementary failure modes. For text insertion, our text-span-level reward jointly assesses semantic fidelity and target-span coverage, penalizes spatial conflicts with products and existing text, and employs a gated structural constraint to preserve non-text regions. For text replacement, our glyph-level reward leverages the connectionist temporal classification (CTC) posterior of the target character to provide graded supervision for fine-grained defects, including missing strokes, structural deformations, and confusion among visually similar characters. We further introduce OpenTextEdit, a dataset comprising 100K images for text editing in product posters, with multi-text layouts, detailed text attributes, product masks, and challenging low-frequency characters. Extensive experiments on both insertion and replacement demonstrate that TextRefine consistently outperforms the evaluated image editing baselines in textual fidelity, placement reliability, and glyph quality while better preserving source-image content.

Figures & tables

Figure 1: Representative failure modes of existing methods for text editing in product posters. In text insertion (top), generated text may overlap salient product regions, conflict with existing text, or deviate from the intended content. In text replacement (bottom), models may alter unintended character instances or produce structurally distorted glyphs.

Figure 2: Overview of the data construction pipeline for text insertion and text replacement in OpenTextEdit.

Figure 3: Valid-span filtering for product-poster text insertion. A detected span is retained when its overlap with pre-existing text is below $\tau_{\mathrm{bbox}}$ and its overlap with the salient-product mask $m$ is below $\tau_{\mathrm{mask}}$ . This criterion implements the non-overlap preference used in our target setting rather than a universal poster-layout rule.

Figure 4: Comparison of binary OCR correctness and the proposed graded glyph signal. Whereas a binary reward retains only the final correctness decision, the target-glyph CTC posterior preserves continuous recognition evidence and provides a more informative signal for optimizing glyph fidelity.

Figure 5: Qualitative comparison of TextRefine with the evaluated baselines on product-poster text insertion (top) and localized replacement (bottom). TextRefine more faithfully renders the requested content while respecting the target poster layout and character structure; baseline outputs exhibit product occlusion, unintended text, or glyph distortion.

Method	Match (%) $\uparrow$	Partial (%) $\downarrow$	Miss (%) $\downarrow$	Extra $\downarrow$	Score $\uparrow$	FID $\downarrow$	SSIM $\uparrow$	PSNR $\uparrow$
Simple Template Prompt (629 samples)
Qwen-Image-Edit-2511	76.8	8.3	14.9	26	0.7829	36.7330	0.8016	14.8536
FireRed-Image-Edit-1.0	82.2	6.2	11.6	32	0.8277	30.5686	0.7825	14.9120
TextRefine (SFT)	87.1	7.2	5.7	52	0.8526	24.3464	0.8232	15.9917
TextRefine (RL)	91.9	4.3	3.8	18	0.9174	24.2191	0.8303	16.0774
Descriptive Prompt (1235 samples)

Table 1: Quantitative comparison on text insertion benchmark under simple template prompt (top, 629 samples) and descriptive prompt (bottom, 1235 samples).

\uparrow

: higher is better;

\downarrow

: lower is better. Best results in bold .

Method	Acc (%) $\uparrow$
Qwen-Image-Edit-2511	48.0
FireRed-Image-Edit-1.0	55.5
TextRefine (SFT)	69.5
TextRefine (RL)	74.5

Table 2: Accuracy on the localized rare-character editing benchmark (200 samples). Higher is better. Best result in bold .

Method	Acc (%) $\uparrow$
SFT (No RL)	69.5
Binary OCR Reward	73.0
$R_{\mathrm{glyph}}$ (Ours)	76.0

Table 4: Ablation on localized text replacement rewards (rare-character benchmark, 200 samples). Higher is better. Best results in bold .

Category	Share (%)
Wearable and Fashion Goods	24.6
Home and Lifestyle Products	21.8
Food and Agricultural Commodities	19.3
Beauty and Personal Care Products	17.9
Digital and General Consumer Goods	16.4

Table 1: Product-category distribution of the approximately 100K-example training set.

Category	Share (%)
Wearable and Fashion Goods	24.5
Home and Lifestyle Products	22.0
Food and Agricultural Commodities	19.5
Beauty and Personal Care Products	18.0
Digital and General Consumer Goods	16.0

Table 2: Product-category distribution of the 200-example evaluation benchmark.

Task	Samples	Share (%)
TI-S	45,829	27.90
TI-D	30,408	18.51
TR-G	10,000	6.09
TR-R	61,148	37.23
RM	16,862	10.27
Total	164,247	100.00

Table 3: Task composition of the SFT training corpus.

Figure 1: Representative training examples for the five SFT tasks.

Figure 2: Representative evaluation examples across the five product domains.

Method	Match $\uparrow$	Partial $\downarrow$	Miss $\downarrow$	Extra $\downarrow$	Score $\uparrow$	Acc. $\uparrow$
PosterMaker	83.4	16.6	0.0	0	0.9685	64.5
AnyText v2	76.4	23.6	0.0	0	0.9552	56.0
QIE	77.1	6.4	16.6	92	0.8361	57.5
FireRed	85.4	5.6	9.1	70	0.9109	67.0
TR-SFT	89.8	6.1	4.1	124	0.9536	72.0
GPT-I2	96.2	3.3	0.5	22	0.9889	87.5

Table 4: Simple-template prompt under the SI protocol.

Method	Match $\uparrow$	Partial $\downarrow$	Miss $\downarrow$	Extra $\downarrow$	Score $\uparrow$	Acc. $\uparrow$
PosterMaker	73.4	26.6	0.0	0	0.9496	48.0
AnyText v2	55.2	44.8	0.0	0	0.9148	34.0
QIE	55.8	11.3	32.9	147	0.6933	35.0
FireRed	75.4	10.5	14.1	106	0.8551	50.5
TR-SFT	87.1	8.3	4.6	135	0.9443	63.5
GPT-I2	94.1	5.1	0.8	26	0.9824	82.5

Table 5: Simple-template prompt under the SP protocol.

Method	Match $\uparrow$	Partial $\downarrow$	Miss $\downarrow$	Extra $\downarrow$	Score $\uparrow$	Acc. $\uparrow$
PosterMaker	90.0	10.0	0.0	0	0.9810	69.0
AnyText v2	84.2	15.8	0.0	0	0.9700	49.5
QIE	84.7	4.2	11.1	114	0.8774	50.5
FireRed	91.8	3.7	4.6	97	0.9431	71.5
TR-SFT	88.8	4.6	6.7	86	0.9256	62.0
GPT-I2	97.1	2.3	0.6	38	0.9900	87.0

Table 6: Descriptive prompt under the SI protocol.

Method	Match $\uparrow$	Partial $\downarrow$	Miss $\downarrow$	Extra $\downarrow$	Score $\uparrow$	Acc. $\uparrow$
PosterMaker	88.0	12.0	0.0	0	0.9772	63.5
AnyText v2	81.6	18.4	0.0	0	0.9650	44.0
QIE	82.0	6.7	11.3	118	0.8718	45.5
FireRed	89.9	5.2	4.9	105	0.9373	66.5
TR-SFT	86.1	6.7	7.2	90	0.9157	56.5
GPT-I2	95.9	3.4	0.7	42	0.9868	82.0

Table 7: Descriptive prompt under the SP protocol.

Method	Acc. (%) $\uparrow$
QIE	45.0
FireRed	52.0
TR-SFT	77.0
QIE-RL	67.0
FireRed-RL	62.5
TR-RL	85.0

Table 8: Additional comparison of reward optimization for text replacement.

Metric	QIE	FireRed	TR-RL	GPT-I2
Add	4.37	4.52	4.45	4.70
Adjust	3.59	3.93	3.61	4.12
Background	3.71	4.23	3.92	4.48
Beauty	2.82	2.66	3.03	4.00
Color	3.84	4.12	3.83	4.37
Compose	3.46	3.49	3.60	4.07

Table 9: General image-editing results on RedBench-EN.

Metric	QIE	FireRed	TR-RL	GPT-I2
Add	4.43	4.48	4.42	4.67
Adjust	3.44	3.90	3.67	4.07
Background	3.94	4.35	4.02	4.45
Beauty	2.79	2.52	3.02	3.91
Color	3.71	3.94	3.86	4.40
Compose	3.35	3.63	3.69	4.11

Table 10: General image-editing results on RedBench-CN.

Figure 3: Qualitative comparison before and after applying the add-text reward to Qwen-Image-Edit-2511.

Figure 4: Qualitative comparison before and after applying the add-text reward to FireRed-Image-Edit-1.0.

Figure 5: Qualitative comparison of TextRefine before and after reinforcement learning on the add-text task.

Figure 6: Qualitative comparisons of Qwen-Image-Edit-2511 and FireRed-Image-Edit-1.0 before and after reinforcement learning on the localized text-replacement task.

Figure 7: Qualitative comparison of TextRefine before and after reinforcement learning on the localized text-replacement task.

Image Editing

Image editing changes selected visual content while preserving the parts of an image that should remain unchanged.

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WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing

Hui Zhang, Juntao Liu, Zongkai Liu, Liqiang Niu+3

Institute of Trustworthy Embodied AI, Fudan University · Shanghai Key Laboratory of Multimodal Embodied AI · Weixin AI, Tencent

Image Editing Text-Guided Image Editing Image Editing Evaluation

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Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors, followed by a multi-objective reinforcement learning stage to align generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.

Abstract

Image Editing

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