# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
### Text Spotting  
Text spotting systems perform text detection and recognition in an end-to-end way. The base idea is to share the same CNN feature extractor with the detector and classifier. For fast-oriented text spotting, FOTS [145] applied the RoIRotate operator from convolution feature maps. Like the MOSTL text detector, [PERSON] et al. [146] proposed a unified framework that also used improved ReLU and inception blocks. The new LWDP algorithm enhanced the character recognition results. The suggested approach could handle many font sizes and text orientations, including vertical texts. TextDragon [147], inspired by TextSnake, used the differentiable operator named RoISlide for connecting arbitrary-shaped text detection and recognition. In ABCNet [148] model, the Bezier curve was introduced to describe arbitrary text instances and BezierAlign to extract accurate features. The model was refined, generating an accurate real-time text spotter. The ABCNet V2 [149] considered the bidirectional multi-scale features, inserted the character attention module without requiring character-level annotation and integrated an adaptive end-to-end training strategy. The ARTS [150] framework enhanced these end-to-end methods, propagating the recognition loss back into the detection branch with auto-rectification module. Mask TextSpotter [151] was a segmentation model influenced by Mask R-CNN. The detection and recognition are at the character level. Since character-level annotations are typically unavailable in public databases, Mask TextSpotter V3 [152] addressed this limitation, introducing the Segmentation Proposal Network (SPN). Both methods incorporated the RoIAlign to conserve more precise information. Another model inspired by Mask R-CNN is suggested in [153]. Considering the authors that demonstrate that feature rectification degrades the performance of irregular shape texts, the feature rectification step was ignored, with it being enough to separate text from the background. MANGO [154] introduced the position-aware mask attention module to perceive the position of text instances, allowing it to retain the global spatial features. Character-level annotations are unnecessary, and the RoI operation to link text detection and recognition was removed. Another framework free from RoI operations is the TExt Spotting TRANSF (TESTR) [155]; furthermore, heuristics-driven post-processing techniques are excluded in the single-encoder dual-decoder framework. The model is suitable for both the polygonal and Bezier curve annotations. To overcome the challenge of determining the space between words, [PERSON] et al. proposed the AE TextSpotter [156] framework, which learns both linguistic and visual representations. SwinTextSpotter [157], a transformer-based model, unified the two tasks with the recognition conversion mechanism, leveraging the synergy of both. This contrasts with approaches that typically share the backbone to integrate detection and recognition.  
The recent text-spotting methods combined text detection and text recognition problems in order to optimise these two related tasks in a unified pipeline. When the system works independently, the text detection errors interfere with text recognition, but in a cooperative system, the text recogniser may reduce the false detections or even enhance the limits of text region. The most efficient technique of information sharing across the modules has been researched, and the first developments are currently being made. Table 9 depicts the performance of several text spotting algorithms on the ICDAR2015 dataset, using three types of lexicons, and Table 10 deepens the comparison of models on the Total Text dataset.