# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
### Text Recognition  
In this subsection, we briefly describe methods that recognise both regular and irregular texts. ASTER [131], an updated version of RARE [132], employed a rectification module based on Spatial Transform Networks to rectify irregular texts and recognise arbitrary shape texts. ScRN [133] achieved better results with the symmetry-constrained rectification module. Nevertheless, the curved text with terminal letters that are almost horizontally oriented and close to the edges of the image causes trouble for the rectification module. CA-FCN [134] indicated the characters at the pixel level. The character attention mechanism, joined with the word formation module, predicts the position and recognises the script. However, the performance tends to suffer without a sequence learning approach. The aforementioned issue is addressed in [135]. The model extracts high-level 2D spatial features and transforms them into a 1D feature sequence. Using word-level annotation, [PERSON] et al. [136] applied the two-dimensional attention module to handle the complex spatial layout of irregular text. To tackle the attention drift and inappropriate threshold selection on segmentation maps, [PERSON] et al. suggested the TextScanner [137]. The algorithm has the advantage of being fast and adequate for long text. Character-level annotations can aid TextScanner to be more efficient when pre-training on artificial data. Considering that text recognition depends on visual perception information and high-level text semantic context understanding, the SRN network [138] incorporated the global semantic reasoning module (GSRM), parallel visual attention module (PVAM) and visual-semantic fusion decoder (VSFD). DPAN [139] differs from the previous parallel-decoupled encoder-decoder framework, designing a dual parallel attention network to alleviate visual misalignment in hard samples. CDistNet [140] is a transformer-based encoder-decoder framework that integrates character feature interactions among visual, semantic and position spaces for recognising more difficult texts. Linguistic knowledge proved to contribute to the refinement of character sequences; therefore, the MATRN model [141] combined language-aware visual and semantic features, exploring the multiple combinations of multi-modal processes with bi-directional fusion to enhance each feature. Visual and linguistic features were combined in [142] to be robust to different language texts. The suggested model (S-GTR) is a Graph Convolutional Network for textual reasoning that joins the pixels based on their spatial context similarity. In order to use bidirectional language context, the methods implemented a two-stage ensemble approach. To overcome the time constraints of these methods, [PERSON] and [PERSON] designed a simple model that uses permutation language modelling, called PARSeq [143]. The context-free and context-aware autoregressive inference is unified and refined using bidirectional context. In contrast to sequence-to-sequence and segmentation-based approaches, [PERSON] et al. [144] proposed a classification-based model with similar results, the CSTR. The model's merits include simplicity and a lack of costly character-level annotations. Multi-orientation, perspective distortion, diversity of the internal properties of texy regions (font, size, shape, space), variable length and multilingual content are only a few of the difficulties that text recognition must overcome. The introduction of a list of words for each image was initially suggested to improve the accuracy of results. Given the limited applicability, some researchers incorporate language models to expand the number of possibilities and rectify the predictions according to prior knowledge. The strategy increases the computational costs and limits the application for that specific language. Besides multi-lingual text, the long arbitrarily shaped text is another unsolved challenge that requires more data to investigate this subject. A balance between speed and accuracy is always required.